Assessment of position and posture by a physical therapist can help reduce and prevent injury in endoscopists, based on data from a pilot study of eight individuals.

romaset/Getty Images

Musculoskeletal injuries among endoscopists are gaining more attention: One technical review indicated that the “prevalence of musculoskeletal pain or injuries ranged from 29% to 89% of gastroenterologists.” While data on avoiding musculoskeletal injury related to endoscopy are limited, recognition of the role of ergonomics is increasing, Stacy A. Markwell, a physical therapist in Chapel Hill, N.C., and colleagues, wrote in a study published in Gastrointestinal Endoscopy.

The mental concentration required along with the physical demands on manipulating the scope have been shown to negatively impact posture, the researchers noted.

The researchers reviewed data from eight endoscopists who were aged 32-71 years; they had a range of clinical experience and were performing 6-30 colonoscopies and 3-21 upper endoscopies per week.

These endoscopists volunteered for an ergonomic intervention involving use of an individualized wellness plan. They completed the Nordic Musculoskeletal Questionnaire to evaluate musculoskeletal complaints during the past 12 months and the past 7 days. Three of the eight participants reported pain at work at initial assessment, which often worsened over the course of the day, and five mentioned fatigue while working. They specified 22 pain sites, mainly in the neck and back. In addition, participants were photographed to evaluate posture in a static position and self-selected “tired” positions.

“When frequent or consistent posturing resulted in suboptimal joint alignment, muscle length, loading at end range of muscle or joints, and/or prolonged static active positioning, participants were photographed to provide personalized feedback for wellness education,” the researchers wrote.

The physical therapist used information from the evaluation and photographs to develop individual plans to improve the ergonomics of the endoscopic suite with adjustments to the location of the bed and positioning of chairs, standing surfaces, and monitors and keyboards. In addition to adjusting the endoscopic suite, the physical therapist developed individual wellness plans including exercises to relieve pain and improve posture, as well as pain education to help clinicians recognize and manage pain and fatigue.

By the end of the study, in a follow-up 6-12 months after the wellness intervention, 63% of pain sites (14 of 22) reported by participants were reduced in intensity or resolved, 32% were unchanged (7 of 22), and 4% increased (1 of 22).

Overall, seven of the eight participants said that the pictures of their posture along with the movement analysis was helpful, and three participants asked for reassessment by the physical therapist. In this study, the average cost of the wellness program was $500.

“All endoscopists reported that the wellness plan was helpful, with procedure suite and posture recommendations being the most beneficial,” the researchers reported. “Upon gaining insight with visualization of their posture and movement during endoscopy, participants’ understanding and motivation to make corrections was intensified.”

The study findings were limited by several factors including the small size, use of a single physical therapist, short follow-up, lack of controls, and use of a single site, the researchers noted. However, “our study provides a detailed, pragmatic, and reproducible framework for performing an individualized physical therapist–directed comprehensive assessment and personalized wellness plan in the workplace to help meet the challenges of ergonomics in endoscopy.”
 

Recognition of the value of ergonomics is rising

“Endoscopy related injury and disability is a known hazard of our profession,” said Gyanprakash A. Ketwaroo, MD, of Baylor College of Medicine, Houston, in an interview. “Any studies to assess and, more importantly, offer ways to prevent such injury are immediately relevant. In this context, ergonomics for endoscopy is an increasing area of research.”

Dr. Ketwaroo said that the study results were not surprising. “I agree with authors that there is a paucity of general ergonomic training and assessment. Specific individualized wellness plans are rare. Developing an individual plan based on observation by physical therapists, and taking into account baseline injury or predisposition to injury would be expected to be more high yield for preventing injury and improving performance

“I believe the main take-home message from the study is that an individualized ergonomic plan based on assessment and feedback by physical therapists appears promising for optimizing endoscopic performance to minimize injury and reduce fatigue,” Dr. Ketwaroo said. However, “long-term studies in much larger samples will be needed to document objective findings of reduced injury or fatigue.”

The study received no outside funding. The researchers had no financial conflicts to disclose. Dr. Ketwaroo serves on the GI & Hepatology News editorial advisory board.

Assessment of position and posture by a physical therapist can help reduce and prevent injury in endoscopists, based on data from a pilot study of eight individuals.

romaset/Getty Images

Musculoskeletal injuries among endoscopists are gaining more attention: One technical review indicated that the “prevalence of musculoskeletal pain or injuries ranged from 29% to 89% of gastroenterologists.” While data on avoiding musculoskeletal injury related to endoscopy are limited, recognition of the role of ergonomics is increasing, Stacy A. Markwell, a physical therapist in Chapel Hill, N.C., and colleagues, wrote in a study published in Gastrointestinal Endoscopy.

The mental concentration required along with the physical demands on manipulating the scope have been shown to negatively impact posture, the researchers noted.

The researchers reviewed data from eight endoscopists who were aged 32-71 years; they had a range of clinical experience and were performing 6-30 colonoscopies and 3-21 upper endoscopies per week.

These endoscopists volunteered for an ergonomic intervention involving use of an individualized wellness plan. They completed the Nordic Musculoskeletal Questionnaire to evaluate musculoskeletal complaints during the past 12 months and the past 7 days. Three of the eight participants reported pain at work at initial assessment, which often worsened over the course of the day, and five mentioned fatigue while working. They specified 22 pain sites, mainly in the neck and back. In addition, participants were photographed to evaluate posture in a static position and self-selected “tired” positions.

“When frequent or consistent posturing resulted in suboptimal joint alignment, muscle length, loading at end range of muscle or joints, and/or prolonged static active positioning, participants were photographed to provide personalized feedback for wellness education,” the researchers wrote.

The physical therapist used information from the evaluation and photographs to develop individual plans to improve the ergonomics of the endoscopic suite with adjustments to the location of the bed and positioning of chairs, standing surfaces, and monitors and keyboards. In addition to adjusting the endoscopic suite, the physical therapist developed individual wellness plans including exercises to relieve pain and improve posture, as well as pain education to help clinicians recognize and manage pain and fatigue.

By the end of the study, in a follow-up 6-12 months after the wellness intervention, 63% of pain sites (14 of 22) reported by participants were reduced in intensity or resolved, 32% were unchanged (7 of 22), and 4% increased (1 of 22).

Overall, seven of the eight participants said that the pictures of their posture along with the movement analysis was helpful, and three participants asked for reassessment by the physical therapist. In this study, the average cost of the wellness program was $500.

“All endoscopists reported that the wellness plan was helpful, with procedure suite and posture recommendations being the most beneficial,” the researchers reported. “Upon gaining insight with visualization of their posture and movement during endoscopy, participants’ understanding and motivation to make corrections was intensified.”

The study findings were limited by several factors including the small size, use of a single physical therapist, short follow-up, lack of controls, and use of a single site, the researchers noted. However, “our study provides a detailed, pragmatic, and reproducible framework for performing an individualized physical therapist–directed comprehensive assessment and personalized wellness plan in the workplace to help meet the challenges of ergonomics in endoscopy.”
 

Recognition of the value of ergonomics is rising

“Endoscopy related injury and disability is a known hazard of our profession,” said Gyanprakash A. Ketwaroo, MD, of Baylor College of Medicine, Houston, in an interview. “Any studies to assess and, more importantly, offer ways to prevent such injury are immediately relevant. In this context, ergonomics for endoscopy is an increasing area of research.”

Dr. Ketwaroo said that the study results were not surprising. “I agree with authors that there is a paucity of general ergonomic training and assessment. Specific individualized wellness plans are rare. Developing an individual plan based on observation by physical therapists, and taking into account baseline injury or predisposition to injury would be expected to be more high yield for preventing injury and improving performance

“I believe the main take-home message from the study is that an individualized ergonomic plan based on assessment and feedback by physical therapists appears promising for optimizing endoscopic performance to minimize injury and reduce fatigue,” Dr. Ketwaroo said. However, “long-term studies in much larger samples will be needed to document objective findings of reduced injury or fatigue.”

The study received no outside funding. The researchers had no financial conflicts to disclose. Dr. Ketwaroo serves on the GI & Hepatology News editorial advisory board.

Assessment of position and posture by a physical therapist can help reduce and prevent injury in endoscopists, based on data from a pilot study of eight individuals.

romaset/Getty Images

Musculoskeletal injuries among endoscopists are gaining more attention: One technical review indicated that the “prevalence of musculoskeletal pain or injuries ranged from 29% to 89% of gastroenterologists.” While data on avoiding musculoskeletal injury related to endoscopy are limited, recognition of the role of ergonomics is increasing, Stacy A. Markwell, a physical therapist in Chapel Hill, N.C., and colleagues, wrote in a study published in Gastrointestinal Endoscopy.

The mental concentration required along with the physical demands on manipulating the scope have been shown to negatively impact posture, the researchers noted.

The researchers reviewed data from eight endoscopists who were aged 32-71 years; they had a range of clinical experience and were performing 6-30 colonoscopies and 3-21 upper endoscopies per week.

These endoscopists volunteered for an ergonomic intervention involving use of an individualized wellness plan. They completed the Nordic Musculoskeletal Questionnaire to evaluate musculoskeletal complaints during the past 12 months and the past 7 days. Three of the eight participants reported pain at work at initial assessment, which often worsened over the course of the day, and five mentioned fatigue while working. They specified 22 pain sites, mainly in the neck and back. In addition, participants were photographed to evaluate posture in a static position and self-selected “tired” positions.

“When frequent or consistent posturing resulted in suboptimal joint alignment, muscle length, loading at end range of muscle or joints, and/or prolonged static active positioning, participants were photographed to provide personalized feedback for wellness education,” the researchers wrote.

The physical therapist used information from the evaluation and photographs to develop individual plans to improve the ergonomics of the endoscopic suite with adjustments to the location of the bed and positioning of chairs, standing surfaces, and monitors and keyboards. In addition to adjusting the endoscopic suite, the physical therapist developed individual wellness plans including exercises to relieve pain and improve posture, as well as pain education to help clinicians recognize and manage pain and fatigue.

By the end of the study, in a follow-up 6-12 months after the wellness intervention, 63% of pain sites (14 of 22) reported by participants were reduced in intensity or resolved, 32% were unchanged (7 of 22), and 4% increased (1 of 22).

Overall, seven of the eight participants said that the pictures of their posture along with the movement analysis was helpful, and three participants asked for reassessment by the physical therapist. In this study, the average cost of the wellness program was $500.

“All endoscopists reported that the wellness plan was helpful, with procedure suite and posture recommendations being the most beneficial,” the researchers reported. “Upon gaining insight with visualization of their posture and movement during endoscopy, participants’ understanding and motivation to make corrections was intensified.”

The study findings were limited by several factors including the small size, use of a single physical therapist, short follow-up, lack of controls, and use of a single site, the researchers noted. However, “our study provides a detailed, pragmatic, and reproducible framework for performing an individualized physical therapist–directed comprehensive assessment and personalized wellness plan in the workplace to help meet the challenges of ergonomics in endoscopy.”
 

Recognition of the value of ergonomics is rising

“Endoscopy related injury and disability is a known hazard of our profession,” said Gyanprakash A. Ketwaroo, MD, of Baylor College of Medicine, Houston, in an interview. “Any studies to assess and, more importantly, offer ways to prevent such injury are immediately relevant. In this context, ergonomics for endoscopy is an increasing area of research.”

Dr. Ketwaroo said that the study results were not surprising. “I agree with authors that there is a paucity of general ergonomic training and assessment. Specific individualized wellness plans are rare. Developing an individual plan based on observation by physical therapists, and taking into account baseline injury or predisposition to injury would be expected to be more high yield for preventing injury and improving performance

“I believe the main take-home message from the study is that an individualized ergonomic plan based on assessment and feedback by physical therapists appears promising for optimizing endoscopic performance to minimize injury and reduce fatigue,” Dr. Ketwaroo said. However, “long-term studies in much larger samples will be needed to document objective findings of reduced injury or fatigue.”

The study received no outside funding. The researchers had no financial conflicts to disclose. Dr. Ketwaroo serves on the GI & Hepatology News editorial advisory board.

Treatment with tocilizumab (Actemra) could stabilize or improve lung function in people with early interstitial lung disease associated with systemic sclerosis (SSc-ILD), a new study has found.

goa_novi/ThinkStock

A paper published online Feb. 3 in Arthritis & Rheumatology presents the results of a post hoc analysis of data from a phase 3, placebo-controlled, double-blind trial of subcutaneous tocilizumab in patients with SSc and progressive skin disease, which included high-resolution chest CT to assess lung involvement and fibrosis.

Tocilizumab is a monoclonal antibody that targets interleukin-6 and is currently approved for the treatment of immune-mediated diseases such as rheumatoid arthritis, giant cell arteritis, cytokine release syndrome, and systemic and polyarticular course juvenile idiopathic arthritis.

Two previous studies of tocilizumab in patients with early, diffuse cutaneous SSc had also found that the treatment was associated with preservation of lung function but did not characterize that effect using radiography.

Of the 210 participants in the trial, called focuSSced, 136 were found to have interstitial lung disease at baseline and were randomized to 162 mg tocilizumab weekly or placebo for 48 weeks.

At baseline, around three-quarters of those with interstitial lung disease had moderate to severe lung involvement, defined as ground glass opacities, honeycombing, and fibrotic reticulation across at least 20% of the whole lung.

Those in the tocilizumab group showed a 0.1% mean decline in forced vital capacity (FVC) over the 48-week study, while those in the placebo group had a mean decline of 6.3%.

When stratified by severity of lung involvement, those with mild lung disease group treated with tocilizumab had a 4.1% decline in FVC, compared with a 10% decline in the placebo group; those with moderate disease in the treatment group had an 0.7% mean increase in FVC, compared with a 5.7% decrease in the placebo group, and those with severe lung involvement in the treatment arm had a 2.1% increase in FVC, compared with a 6.7% decrease in the placebo arm.

Those treated with tocilizumab also showed a statistically significant 1.8% improvement in the amount of lung involvement, which was largely seen in those with more extensive lung involvement at baseline. Those with more than 20% of the lung affected had a significant 4.9% reduction in lung area affected, while those in the placebo arm showed a significant increase in fibrosis.

First author David Roofeh, MD, of the University of Michigan Scleroderma Program, and colleagues wrote that most patients with SSc will develop interstitial lung disease – particularly those with early, diffuse cutaneous SSc and elevated markers such as C-reactive protein.

“Patients with these high-risk features, especially those with disease in the initial phase of development, represent an important target for early intervention as ILD is largely irreversible in SSc,” the authors wrote.

Findings from a specific patient population may not be generalizable

Commenting on the findings, Lorinda Chung, MD, of Stanford (Calif.) University, said in an interview that the study demonstrated that tocilizumab could prevent radiographic progression of ILD in early diffuse SSc patients with mild to severe lung disease and evidence of active skin disease, as well as elevated inflammatory markers.

“This was a very specific patient population who was studied in the focuSSced clinical trial, and this paper only evaluated a subset of these patients,” Dr. Chung said. “The results may not be generalizable to all SSc-ILD patients and further studies are needed.”

The authors suggested that the patients with progressive skin disease and elevated acute phase reactants may represent a group in the immunoinflammatory phase of the disease rather than the advanced fibrotic stage, and that this might be a “window of therapeutic opportunity to preserve lung function.”

Dr. Chung noted that the radiographic improvement induced by tocilizumab treatment was greatest in those with the most radiographic disease at baseline.

“This may reflect tocilizumab’s impact on decreasing inflammation, but we are not provided the data on the effects of tocilizumab on the individual components of the QILD [quantitative ILD: summation of ground glass opacities, honeycombing, and fibrotic reticulation],” she said.

The study’s authors also made a point about the utility of screening patients with high-resolution chest CT to detect early signs of ILD.

“Our data demonstrate the value of obtaining HRCT at the time of diagnosis: PFTs [pulmonary function tests] are not sensitive enough to accurately assess the presence of ILD and delays in treatment initiation may lead to irreversible disease,” they wrote.

Describing the results as ‘hypothesis-generating’ owing to the post hoc nature of the analysis, the authors said that FVC was an indirect measure of the flow-resistive properties of the lung, and that other aspects of SSc – such as hide-bound chest thickness – could cause thoracic restriction.

Two authors were funded by the National Institutes of Health. Six authors declared grants, funding, and other support from the pharmaceutical sector, including Roche, which sponsored the original focuSSced trial.

Treatment with tocilizumab (Actemra) could stabilize or improve lung function in people with early interstitial lung disease associated with systemic sclerosis (SSc-ILD), a new study has found.

goa_novi/ThinkStock

A paper published online Feb. 3 in Arthritis & Rheumatology presents the results of a post hoc analysis of data from a phase 3, placebo-controlled, double-blind trial of subcutaneous tocilizumab in patients with SSc and progressive skin disease, which included high-resolution chest CT to assess lung involvement and fibrosis.

Tocilizumab is a monoclonal antibody that targets interleukin-6 and is currently approved for the treatment of immune-mediated diseases such as rheumatoid arthritis, giant cell arteritis, cytokine release syndrome, and systemic and polyarticular course juvenile idiopathic arthritis.

Two previous studies of tocilizumab in patients with early, diffuse cutaneous SSc had also found that the treatment was associated with preservation of lung function but did not characterize that effect using radiography.

Of the 210 participants in the trial, called focuSSced, 136 were found to have interstitial lung disease at baseline and were randomized to 162 mg tocilizumab weekly or placebo for 48 weeks.

At baseline, around three-quarters of those with interstitial lung disease had moderate to severe lung involvement, defined as ground glass opacities, honeycombing, and fibrotic reticulation across at least 20% of the whole lung.

Those in the tocilizumab group showed a 0.1% mean decline in forced vital capacity (FVC) over the 48-week study, while those in the placebo group had a mean decline of 6.3%.

When stratified by severity of lung involvement, those with mild lung disease group treated with tocilizumab had a 4.1% decline in FVC, compared with a 10% decline in the placebo group; those with moderate disease in the treatment group had an 0.7% mean increase in FVC, compared with a 5.7% decrease in the placebo group, and those with severe lung involvement in the treatment arm had a 2.1% increase in FVC, compared with a 6.7% decrease in the placebo arm.

Those treated with tocilizumab also showed a statistically significant 1.8% improvement in the amount of lung involvement, which was largely seen in those with more extensive lung involvement at baseline. Those with more than 20% of the lung affected had a significant 4.9% reduction in lung area affected, while those in the placebo arm showed a significant increase in fibrosis.

First author David Roofeh, MD, of the University of Michigan Scleroderma Program, and colleagues wrote that most patients with SSc will develop interstitial lung disease – particularly those with early, diffuse cutaneous SSc and elevated markers such as C-reactive protein.

“Patients with these high-risk features, especially those with disease in the initial phase of development, represent an important target for early intervention as ILD is largely irreversible in SSc,” the authors wrote.

Findings from a specific patient population may not be generalizable

Commenting on the findings, Lorinda Chung, MD, of Stanford (Calif.) University, said in an interview that the study demonstrated that tocilizumab could prevent radiographic progression of ILD in early diffuse SSc patients with mild to severe lung disease and evidence of active skin disease, as well as elevated inflammatory markers.

“This was a very specific patient population who was studied in the focuSSced clinical trial, and this paper only evaluated a subset of these patients,” Dr. Chung said. “The results may not be generalizable to all SSc-ILD patients and further studies are needed.”

The authors suggested that the patients with progressive skin disease and elevated acute phase reactants may represent a group in the immunoinflammatory phase of the disease rather than the advanced fibrotic stage, and that this might be a “window of therapeutic opportunity to preserve lung function.”

Dr. Chung noted that the radiographic improvement induced by tocilizumab treatment was greatest in those with the most radiographic disease at baseline.

“This may reflect tocilizumab’s impact on decreasing inflammation, but we are not provided the data on the effects of tocilizumab on the individual components of the QILD [quantitative ILD: summation of ground glass opacities, honeycombing, and fibrotic reticulation],” she said.

The study’s authors also made a point about the utility of screening patients with high-resolution chest CT to detect early signs of ILD.

“Our data demonstrate the value of obtaining HRCT at the time of diagnosis: PFTs [pulmonary function tests] are not sensitive enough to accurately assess the presence of ILD and delays in treatment initiation may lead to irreversible disease,” they wrote.

Describing the results as ‘hypothesis-generating’ owing to the post hoc nature of the analysis, the authors said that FVC was an indirect measure of the flow-resistive properties of the lung, and that other aspects of SSc – such as hide-bound chest thickness – could cause thoracic restriction.

Two authors were funded by the National Institutes of Health. Six authors declared grants, funding, and other support from the pharmaceutical sector, including Roche, which sponsored the original focuSSced trial.

Treatment with tocilizumab (Actemra) could stabilize or improve lung function in people with early interstitial lung disease associated with systemic sclerosis (SSc-ILD), a new study has found.

goa_novi/ThinkStock

A paper published online Feb. 3 in Arthritis & Rheumatology presents the results of a post hoc analysis of data from a phase 3, placebo-controlled, double-blind trial of subcutaneous tocilizumab in patients with SSc and progressive skin disease, which included high-resolution chest CT to assess lung involvement and fibrosis.

Tocilizumab is a monoclonal antibody that targets interleukin-6 and is currently approved for the treatment of immune-mediated diseases such as rheumatoid arthritis, giant cell arteritis, cytokine release syndrome, and systemic and polyarticular course juvenile idiopathic arthritis.

Two previous studies of tocilizumab in patients with early, diffuse cutaneous SSc had also found that the treatment was associated with preservation of lung function but did not characterize that effect using radiography.

Of the 210 participants in the trial, called focuSSced, 136 were found to have interstitial lung disease at baseline and were randomized to 162 mg tocilizumab weekly or placebo for 48 weeks.

At baseline, around three-quarters of those with interstitial lung disease had moderate to severe lung involvement, defined as ground glass opacities, honeycombing, and fibrotic reticulation across at least 20% of the whole lung.

Those in the tocilizumab group showed a 0.1% mean decline in forced vital capacity (FVC) over the 48-week study, while those in the placebo group had a mean decline of 6.3%.

When stratified by severity of lung involvement, those with mild lung disease group treated with tocilizumab had a 4.1% decline in FVC, compared with a 10% decline in the placebo group; those with moderate disease in the treatment group had an 0.7% mean increase in FVC, compared with a 5.7% decrease in the placebo group, and those with severe lung involvement in the treatment arm had a 2.1% increase in FVC, compared with a 6.7% decrease in the placebo arm.

Those treated with tocilizumab also showed a statistically significant 1.8% improvement in the amount of lung involvement, which was largely seen in those with more extensive lung involvement at baseline. Those with more than 20% of the lung affected had a significant 4.9% reduction in lung area affected, while those in the placebo arm showed a significant increase in fibrosis.

First author David Roofeh, MD, of the University of Michigan Scleroderma Program, and colleagues wrote that most patients with SSc will develop interstitial lung disease – particularly those with early, diffuse cutaneous SSc and elevated markers such as C-reactive protein.

“Patients with these high-risk features, especially those with disease in the initial phase of development, represent an important target for early intervention as ILD is largely irreversible in SSc,” the authors wrote.

Findings from a specific patient population may not be generalizable

Commenting on the findings, Lorinda Chung, MD, of Stanford (Calif.) University, said in an interview that the study demonstrated that tocilizumab could prevent radiographic progression of ILD in early diffuse SSc patients with mild to severe lung disease and evidence of active skin disease, as well as elevated inflammatory markers.

“This was a very specific patient population who was studied in the focuSSced clinical trial, and this paper only evaluated a subset of these patients,” Dr. Chung said. “The results may not be generalizable to all SSc-ILD patients and further studies are needed.”

The authors suggested that the patients with progressive skin disease and elevated acute phase reactants may represent a group in the immunoinflammatory phase of the disease rather than the advanced fibrotic stage, and that this might be a “window of therapeutic opportunity to preserve lung function.”

Dr. Chung noted that the radiographic improvement induced by tocilizumab treatment was greatest in those with the most radiographic disease at baseline.

“This may reflect tocilizumab’s impact on decreasing inflammation, but we are not provided the data on the effects of tocilizumab on the individual components of the QILD [quantitative ILD: summation of ground glass opacities, honeycombing, and fibrotic reticulation],” she said.

The study’s authors also made a point about the utility of screening patients with high-resolution chest CT to detect early signs of ILD.

“Our data demonstrate the value of obtaining HRCT at the time of diagnosis: PFTs [pulmonary function tests] are not sensitive enough to accurately assess the presence of ILD and delays in treatment initiation may lead to irreversible disease,” they wrote.

Describing the results as ‘hypothesis-generating’ owing to the post hoc nature of the analysis, the authors said that FVC was an indirect measure of the flow-resistive properties of the lung, and that other aspects of SSc – such as hide-bound chest thickness – could cause thoracic restriction.

Two authors were funded by the National Institutes of Health. Six authors declared grants, funding, and other support from the pharmaceutical sector, including Roche, which sponsored the original focuSSced trial.

A new prognostic gene signature was found to be associated with overall survival of diffuse large B-cell lymphoma (DLBCL) in multiple clinical studies, according to the results of a database analysis.

A total of 33 genes formed the signature that could be transformed into a risk score, according to a study by Santosh Khanal, a senior bioinformatics scientist at Children’s Mercy Kansas City (Mo.), and colleagues published in Cancer Genetics.

Their study used gene expression and clinical parameters from the Lymphoma/Leukemia Molecular Profiling Project from 233 patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) therapy to identify genes whose expression was associated with overall survival (OS). They refined the information to develop prognostic gene signature that could be used to calculate risk scores for each individual and predict OS.
 

Significant separation

The researchers initially found 61 genes individually associated with OS that had a nonadjusted P ≤ .001 using the univariate Cox regression model. The 61 genes were then assessed using multivariate Cox analysis to identify a minimal set of genes that could predict OS, resulting in a minimal set of 33 genes that were used to develop a survival risk score for each individual.

The OS of the high-risk group was significantly reduced, compared with the low-risk group (hazard ratio, 0.046; P < .0001). Upon stratification of individuals by risk score into quartiles, patients in the lowest quartile risk score had a 100% probability of survival, while individuals in the highest quartile had a 9.2% OS by year 5.

In order to validate their results, the researchers calculated risk scores using their prognostic gene set in three additional published DLBCL studies. For all three studies, individuals with low risk score had significantly better OS, “indicating the robustness of the gene signature for multiple external datasets,” according to the researchers.

The top biological pathways and processes that were significantly overrepresented in the 33-gene set were the thioester biosynthetic process (P = .00005), cellular response to hormone stimulus (P = .002), G protein–coupled receptor ligand binding (P = .003), and myeloid cell activation involved in immune responses (P = 0.006).

“As new therapies for lymphoma become available, including new immunotherapies and personalized medicine approaches such as [chimeric antigen receptor] T cells, it will be important to identify candidate individuals that are at high risk and may benefit from experimental therapeutic approaches compared with individuals who will have lower risk of death with current therapies,” the researchers concluded.

The authors reported that they had no competing interests.

[email protected]

A new prognostic gene signature was found to be associated with overall survival of diffuse large B-cell lymphoma (DLBCL) in multiple clinical studies, according to the results of a database analysis.

A total of 33 genes formed the signature that could be transformed into a risk score, according to a study by Santosh Khanal, a senior bioinformatics scientist at Children’s Mercy Kansas City (Mo.), and colleagues published in Cancer Genetics.

Their study used gene expression and clinical parameters from the Lymphoma/Leukemia Molecular Profiling Project from 233 patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) therapy to identify genes whose expression was associated with overall survival (OS). They refined the information to develop prognostic gene signature that could be used to calculate risk scores for each individual and predict OS.
 

Significant separation

The researchers initially found 61 genes individually associated with OS that had a nonadjusted P ≤ .001 using the univariate Cox regression model. The 61 genes were then assessed using multivariate Cox analysis to identify a minimal set of genes that could predict OS, resulting in a minimal set of 33 genes that were used to develop a survival risk score for each individual.

The OS of the high-risk group was significantly reduced, compared with the low-risk group (hazard ratio, 0.046; P < .0001). Upon stratification of individuals by risk score into quartiles, patients in the lowest quartile risk score had a 100% probability of survival, while individuals in the highest quartile had a 9.2% OS by year 5.

In order to validate their results, the researchers calculated risk scores using their prognostic gene set in three additional published DLBCL studies. For all three studies, individuals with low risk score had significantly better OS, “indicating the robustness of the gene signature for multiple external datasets,” according to the researchers.

The top biological pathways and processes that were significantly overrepresented in the 33-gene set were the thioester biosynthetic process (P = .00005), cellular response to hormone stimulus (P = .002), G protein–coupled receptor ligand binding (P = .003), and myeloid cell activation involved in immune responses (P = 0.006).

“As new therapies for lymphoma become available, including new immunotherapies and personalized medicine approaches such as [chimeric antigen receptor] T cells, it will be important to identify candidate individuals that are at high risk and may benefit from experimental therapeutic approaches compared with individuals who will have lower risk of death with current therapies,” the researchers concluded.

The authors reported that they had no competing interests.

[email protected]

A new prognostic gene signature was found to be associated with overall survival of diffuse large B-cell lymphoma (DLBCL) in multiple clinical studies, according to the results of a database analysis.

A total of 33 genes formed the signature that could be transformed into a risk score, according to a study by Santosh Khanal, a senior bioinformatics scientist at Children’s Mercy Kansas City (Mo.), and colleagues published in Cancer Genetics.

Their study used gene expression and clinical parameters from the Lymphoma/Leukemia Molecular Profiling Project from 233 patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) therapy to identify genes whose expression was associated with overall survival (OS). They refined the information to develop prognostic gene signature that could be used to calculate risk scores for each individual and predict OS.
 

Significant separation

The researchers initially found 61 genes individually associated with OS that had a nonadjusted P ≤ .001 using the univariate Cox regression model. The 61 genes were then assessed using multivariate Cox analysis to identify a minimal set of genes that could predict OS, resulting in a minimal set of 33 genes that were used to develop a survival risk score for each individual.

The OS of the high-risk group was significantly reduced, compared with the low-risk group (hazard ratio, 0.046; P < .0001). Upon stratification of individuals by risk score into quartiles, patients in the lowest quartile risk score had a 100% probability of survival, while individuals in the highest quartile had a 9.2% OS by year 5.

In order to validate their results, the researchers calculated risk scores using their prognostic gene set in three additional published DLBCL studies. For all three studies, individuals with low risk score had significantly better OS, “indicating the robustness of the gene signature for multiple external datasets,” according to the researchers.

The top biological pathways and processes that were significantly overrepresented in the 33-gene set were the thioester biosynthetic process (P = .00005), cellular response to hormone stimulus (P = .002), G protein–coupled receptor ligand binding (P = .003), and myeloid cell activation involved in immune responses (P = 0.006).

“As new therapies for lymphoma become available, including new immunotherapies and personalized medicine approaches such as [chimeric antigen receptor] T cells, it will be important to identify candidate individuals that are at high risk and may benefit from experimental therapeutic approaches compared with individuals who will have lower risk of death with current therapies,” the researchers concluded.

The authors reported that they had no competing interests.

[email protected]

Doctors are learning about new rules coming this April that encourage open and transparent communication among patients, families, and clinicians. The rules, putting into effect the bipartisan 21st Century Cures Act, mandate offering patients access to notes (“open notes”) written by clinicians in electronic medical records.

A recent article from this news organization noted that for many doctors this represents both a sudden and troubling change in practice. For others, the rules codify what they have been doing as a matter of routine for a decade. Spurred by the OpenNotes movement, at least 55 million Americans are already offered access to their clinical notes, including, since 2013, more than 9 million veterans with access to the Blue Button function in Veterans Affairs practices and hospitals.

The practice is spreading beyond the United States to other countries, including Canada, Sweden, Norway, Estonia, and the United Kingdom.

In this commentary, we review what patients, clinicians, and policymakers have been learning about open notes.
 

The patient experience

What do patients experience? In a survey of more than 22,000 patients who read notes in three diverse health systems, more than 90% reported having a good grasp of what their doctors and other clinicians had written, and very few (3%) reported being very confused by what they read. About two-thirds described reading their notes as very important for taking care of their health, remembering details of their visits and their care plans, and understanding why a medication was prescribed.

Indeed, in a clinically exciting finding, 14% of survey respondents reported that reading their notes made them more likely to take their medications as their doctors wished. With about half of Americans with chronic illness failing to take their medicines as prescribed, which sometimes leads to compromised outcomes and associated unnecessary costs (estimated at $300 billion annually), these reports of increased adherence should be taken very seriously.

Some doctors anticipate that open notes will erode patient communication. A growing body of research reveals just the opposite. In multiple surveys, patients describe open notes as “extending the visit,” strengthening collaboration and teamwork with their doctor. Quite possibly, the invitation to read notes may in itself increase trust. Such benefits appear especially pronounced among patients who are older, less educated, are persons of color or Hispanic, or who do not speak English at home.

And in several studies, more than a third of patients also report sharing their notes with others, with older and chronically ill patients in particular sharing access with family and friends who are their care partners.

On the other hand, a small minority of patients (5%) do report being more worried by what they read. It’s unknown whether this is because they are better informed about their care or because baseline anxiety levels increase. Doctors expect also that some patients, particularly those with cancer or serious mental illness, will be upset by their notes. So far, evidence does not support that specific concern.

Conversely, withholding, delaying, or blocking notes may be a source of anxiety or even stigmatization. When clinicians find themselves worried about sharing notes, we suggest that they discuss with their patients the benefits and risks. Recall also that transparency facilitates freedom of choice; patients make their own decision, and quite a few choose to leave notes unread.

Finding mistakes early and preventing harm are important goals for health care, and open notes can make care safer. Inevitably, medical records contain errors, omissions, and inaccuracies. In a large patient survey, 21% reported finding an error in their notes, and 42% perceived the error to be serious.

Moreover, 25% of doctors with more than a year’s experience with open notes reported patients finding errors that they (the doctors) considered “serious.” In 2015, the National Academy of Medicine cited open notes as a mechanism for improving diagnostic accuracy. In regard to possible legal action from patients, most attorneys, patients, and doctors agree that more transparent communication will build trust overall and, if anything, diminish litigation. We know of no instances so far of lawsuits deriving from open notes.
 

The physician experience

Doctors may worry that open notes will impede workflow, that they will be compelled to “dumb down” their documentation to avoid causing offense or anxiety, and that patients will demand changes to what is written. Here, extensive survey research should allay such fears and expectations. In a survey of more than 1,600 clinicians with at least 1 year of experience with open notes, reports of disruption to workflow were uncommon.

Most doctors (84%) reported that patients contacted them with questions about their notes “less than monthly or never.” Approximately two-thirds (62%) reported spending the same amount of time writing visit notes.

After implementing open notes, many doctors do report being more mindful about their documentation. For example, 41% reported changing how they used language such as “patient denies” or “noncompliant,” and 18% reported changing their use of medical jargon or abbreviations. Might these changes undermine the utility of medical notes? A majority of doctors surveyed (78%) said no, reporting that, after implementing open notes, the value of their documentation was the same or better.

Innovations spotlight difficult and often longstanding challenges. Open notes highlight the complex role of medical records in preserving privacy, especially in the spectrum of abuse, whether domestic or involving elders, children or sexual transgressions. For families with adolescents, issues concerning confidentiality can become a two-way street, and federal and state rules at times provide conflicting and idiosyncratic guidance. It is important to emphasize that the new rules permit information blocking if there is clear evidence that doing so “will substantially reduce the risk of harm” to patients or to other third parties.

Perhaps think of open notes as a new medicine designed to help the vast majority of those who use it but with side effects and even contraindications for a few. Doctors can step in to minimize risks to vulnerable individuals, and imaginative and creative solutions to complex issues may emerge. In a growing number of practices serving adolescents, clinicians can now create two notes, with some elements of care visible on a patient portal and others held privately or visible only to the adolescent.
 

The shared experience

Overall, when it comes to documenting sensitive social information, open notes may act as a useful catalyst prompting deeper discussion about personal details clinically important to record, as opposed to those perhaps best left unwritten.

The implementation of open notes nationwide calls for exciting explorations. How can transparent systems maximize benefits for targeted populations in diverse settings? For patients with mental illness, can notes become part of the therapy? Given that care partners often report more benefit from reading notes than do patients themselves, how can they be mobilized to maximize their contributions to those acutely ill on hospital floors, or to family members with Alzheimer’s or in long-term care facilities?

How can we harness emerging technologies to translate notes and medical records into other languages or support lower literacy levels, while preserving the clinical detail in the notes? Should patients contribute to their own notes, cogenerating them with their clinicians? Experiments for “OurNotes” interventions are underway, and early reports from both patients and doctors hold considerable promise.

Ownership of medical records is evolving. Once firmly held by clinicians, electronic technologies have rapidly led to what may best be viewed currently as joint ownership by clinicians and patients. As apps evolve further and issues with interoperability of records diminish, it is likely that patients will eventually take control. Then it will be up to patients what to carry in their records. Clinicians will advise, but patients will decide.

The new rules herald clear changes in the fabric of care, and after a decade of study we anticipate that the benefits well outweigh the harms. But in the short run, it’s wrong to predict an avalanche. Two decades ago, when patient portals first revealed laboratory test findings to patients, doctors expected cataclysmic change in their practices. It did not occur. The vast majority of patients who registered on portals benefited and few disturbed their doctors.

Similarly, after notes were first unblinded by the OpenNotes research teams, the question we were asked most commonly by the primary care doctors who volunteered was whether the computers were actually displaying their notes. Even though many patients read them carefully, the doctors heard little from them. Clinicians have now reported the same experience in several subsequent studies.

Patients are resourceful, turning quickly to friends or the Internet for answers to their questions. They know how busy doctors are and don’t want to bother them if at all possible. When notes do trigger questions, the time taken to respond is probably offset by silence from other patients finding answers to their own questions in notes they read.

We believe that clinicians should embrace the spirit of the rules and also view them as HIPAA catching up with a computerized universe. As the new practice takes hold, ambiguities will diminish as further experience and research evolve. Warner V. Slack, MD, the first doctor to ask patients to talk to computers, opined that patients are the “largest and least utilized resource in health care.” Open and transparent communication through electronic medical records may mobilize patients (and their families) far more effectively. Patients will almost certainly benefit. Remembering Dr. Slack’s prophecy, we believe that clinicians will too.

A version of this article first appeared on Medscape.com.

Doctors are learning about new rules coming this April that encourage open and transparent communication among patients, families, and clinicians. The rules, putting into effect the bipartisan 21st Century Cures Act, mandate offering patients access to notes (“open notes”) written by clinicians in electronic medical records.

A recent article from this news organization noted that for many doctors this represents both a sudden and troubling change in practice. For others, the rules codify what they have been doing as a matter of routine for a decade. Spurred by the OpenNotes movement, at least 55 million Americans are already offered access to their clinical notes, including, since 2013, more than 9 million veterans with access to the Blue Button function in Veterans Affairs practices and hospitals.

The practice is spreading beyond the United States to other countries, including Canada, Sweden, Norway, Estonia, and the United Kingdom.

In this commentary, we review what patients, clinicians, and policymakers have been learning about open notes.
 

The patient experience

What do patients experience? In a survey of more than 22,000 patients who read notes in three diverse health systems, more than 90% reported having a good grasp of what their doctors and other clinicians had written, and very few (3%) reported being very confused by what they read. About two-thirds described reading their notes as very important for taking care of their health, remembering details of their visits and their care plans, and understanding why a medication was prescribed.

Indeed, in a clinically exciting finding, 14% of survey respondents reported that reading their notes made them more likely to take their medications as their doctors wished. With about half of Americans with chronic illness failing to take their medicines as prescribed, which sometimes leads to compromised outcomes and associated unnecessary costs (estimated at $300 billion annually), these reports of increased adherence should be taken very seriously.

Some doctors anticipate that open notes will erode patient communication. A growing body of research reveals just the opposite. In multiple surveys, patients describe open notes as “extending the visit,” strengthening collaboration and teamwork with their doctor. Quite possibly, the invitation to read notes may in itself increase trust. Such benefits appear especially pronounced among patients who are older, less educated, are persons of color or Hispanic, or who do not speak English at home.

And in several studies, more than a third of patients also report sharing their notes with others, with older and chronically ill patients in particular sharing access with family and friends who are their care partners.

On the other hand, a small minority of patients (5%) do report being more worried by what they read. It’s unknown whether this is because they are better informed about their care or because baseline anxiety levels increase. Doctors expect also that some patients, particularly those with cancer or serious mental illness, will be upset by their notes. So far, evidence does not support that specific concern.

Conversely, withholding, delaying, or blocking notes may be a source of anxiety or even stigmatization. When clinicians find themselves worried about sharing notes, we suggest that they discuss with their patients the benefits and risks. Recall also that transparency facilitates freedom of choice; patients make their own decision, and quite a few choose to leave notes unread.

Finding mistakes early and preventing harm are important goals for health care, and open notes can make care safer. Inevitably, medical records contain errors, omissions, and inaccuracies. In a large patient survey, 21% reported finding an error in their notes, and 42% perceived the error to be serious.

Moreover, 25% of doctors with more than a year’s experience with open notes reported patients finding errors that they (the doctors) considered “serious.” In 2015, the National Academy of Medicine cited open notes as a mechanism for improving diagnostic accuracy. In regard to possible legal action from patients, most attorneys, patients, and doctors agree that more transparent communication will build trust overall and, if anything, diminish litigation. We know of no instances so far of lawsuits deriving from open notes.
 

The physician experience

Doctors may worry that open notes will impede workflow, that they will be compelled to “dumb down” their documentation to avoid causing offense or anxiety, and that patients will demand changes to what is written. Here, extensive survey research should allay such fears and expectations. In a survey of more than 1,600 clinicians with at least 1 year of experience with open notes, reports of disruption to workflow were uncommon.

Most doctors (84%) reported that patients contacted them with questions about their notes “less than monthly or never.” Approximately two-thirds (62%) reported spending the same amount of time writing visit notes.

After implementing open notes, many doctors do report being more mindful about their documentation. For example, 41% reported changing how they used language such as “patient denies” or “noncompliant,” and 18% reported changing their use of medical jargon or abbreviations. Might these changes undermine the utility of medical notes? A majority of doctors surveyed (78%) said no, reporting that, after implementing open notes, the value of their documentation was the same or better.

Innovations spotlight difficult and often longstanding challenges. Open notes highlight the complex role of medical records in preserving privacy, especially in the spectrum of abuse, whether domestic or involving elders, children or sexual transgressions. For families with adolescents, issues concerning confidentiality can become a two-way street, and federal and state rules at times provide conflicting and idiosyncratic guidance. It is important to emphasize that the new rules permit information blocking if there is clear evidence that doing so “will substantially reduce the risk of harm” to patients or to other third parties.

Perhaps think of open notes as a new medicine designed to help the vast majority of those who use it but with side effects and even contraindications for a few. Doctors can step in to minimize risks to vulnerable individuals, and imaginative and creative solutions to complex issues may emerge. In a growing number of practices serving adolescents, clinicians can now create two notes, with some elements of care visible on a patient portal and others held privately or visible only to the adolescent.
 

The shared experience

Overall, when it comes to documenting sensitive social information, open notes may act as a useful catalyst prompting deeper discussion about personal details clinically important to record, as opposed to those perhaps best left unwritten.

The implementation of open notes nationwide calls for exciting explorations. How can transparent systems maximize benefits for targeted populations in diverse settings? For patients with mental illness, can notes become part of the therapy? Given that care partners often report more benefit from reading notes than do patients themselves, how can they be mobilized to maximize their contributions to those acutely ill on hospital floors, or to family members with Alzheimer’s or in long-term care facilities?

How can we harness emerging technologies to translate notes and medical records into other languages or support lower literacy levels, while preserving the clinical detail in the notes? Should patients contribute to their own notes, cogenerating them with their clinicians? Experiments for “OurNotes” interventions are underway, and early reports from both patients and doctors hold considerable promise.

Ownership of medical records is evolving. Once firmly held by clinicians, electronic technologies have rapidly led to what may best be viewed currently as joint ownership by clinicians and patients. As apps evolve further and issues with interoperability of records diminish, it is likely that patients will eventually take control. Then it will be up to patients what to carry in their records. Clinicians will advise, but patients will decide.

The new rules herald clear changes in the fabric of care, and after a decade of study we anticipate that the benefits well outweigh the harms. But in the short run, it’s wrong to predict an avalanche. Two decades ago, when patient portals first revealed laboratory test findings to patients, doctors expected cataclysmic change in their practices. It did not occur. The vast majority of patients who registered on portals benefited and few disturbed their doctors.

Similarly, after notes were first unblinded by the OpenNotes research teams, the question we were asked most commonly by the primary care doctors who volunteered was whether the computers were actually displaying their notes. Even though many patients read them carefully, the doctors heard little from them. Clinicians have now reported the same experience in several subsequent studies.

Patients are resourceful, turning quickly to friends or the Internet for answers to their questions. They know how busy doctors are and don’t want to bother them if at all possible. When notes do trigger questions, the time taken to respond is probably offset by silence from other patients finding answers to their own questions in notes they read.

We believe that clinicians should embrace the spirit of the rules and also view them as HIPAA catching up with a computerized universe. As the new practice takes hold, ambiguities will diminish as further experience and research evolve. Warner V. Slack, MD, the first doctor to ask patients to talk to computers, opined that patients are the “largest and least utilized resource in health care.” Open and transparent communication through electronic medical records may mobilize patients (and their families) far more effectively. Patients will almost certainly benefit. Remembering Dr. Slack’s prophecy, we believe that clinicians will too.

A version of this article first appeared on Medscape.com.

Doctors are learning about new rules coming this April that encourage open and transparent communication among patients, families, and clinicians. The rules, putting into effect the bipartisan 21st Century Cures Act, mandate offering patients access to notes (“open notes”) written by clinicians in electronic medical records.

A recent article from this news organization noted that for many doctors this represents both a sudden and troubling change in practice. For others, the rules codify what they have been doing as a matter of routine for a decade. Spurred by the OpenNotes movement, at least 55 million Americans are already offered access to their clinical notes, including, since 2013, more than 9 million veterans with access to the Blue Button function in Veterans Affairs practices and hospitals.

The practice is spreading beyond the United States to other countries, including Canada, Sweden, Norway, Estonia, and the United Kingdom.

In this commentary, we review what patients, clinicians, and policymakers have been learning about open notes.
 

The patient experience

What do patients experience? In a survey of more than 22,000 patients who read notes in three diverse health systems, more than 90% reported having a good grasp of what their doctors and other clinicians had written, and very few (3%) reported being very confused by what they read. About two-thirds described reading their notes as very important for taking care of their health, remembering details of their visits and their care plans, and understanding why a medication was prescribed.

Indeed, in a clinically exciting finding, 14% of survey respondents reported that reading their notes made them more likely to take their medications as their doctors wished. With about half of Americans with chronic illness failing to take their medicines as prescribed, which sometimes leads to compromised outcomes and associated unnecessary costs (estimated at $300 billion annually), these reports of increased adherence should be taken very seriously.

Some doctors anticipate that open notes will erode patient communication. A growing body of research reveals just the opposite. In multiple surveys, patients describe open notes as “extending the visit,” strengthening collaboration and teamwork with their doctor. Quite possibly, the invitation to read notes may in itself increase trust. Such benefits appear especially pronounced among patients who are older, less educated, are persons of color or Hispanic, or who do not speak English at home.

And in several studies, more than a third of patients also report sharing their notes with others, with older and chronically ill patients in particular sharing access with family and friends who are their care partners.

On the other hand, a small minority of patients (5%) do report being more worried by what they read. It’s unknown whether this is because they are better informed about their care or because baseline anxiety levels increase. Doctors expect also that some patients, particularly those with cancer or serious mental illness, will be upset by their notes. So far, evidence does not support that specific concern.

Conversely, withholding, delaying, or blocking notes may be a source of anxiety or even stigmatization. When clinicians find themselves worried about sharing notes, we suggest that they discuss with their patients the benefits and risks. Recall also that transparency facilitates freedom of choice; patients make their own decision, and quite a few choose to leave notes unread.

Finding mistakes early and preventing harm are important goals for health care, and open notes can make care safer. Inevitably, medical records contain errors, omissions, and inaccuracies. In a large patient survey, 21% reported finding an error in their notes, and 42% perceived the error to be serious.

Moreover, 25% of doctors with more than a year’s experience with open notes reported patients finding errors that they (the doctors) considered “serious.” In 2015, the National Academy of Medicine cited open notes as a mechanism for improving diagnostic accuracy. In regard to possible legal action from patients, most attorneys, patients, and doctors agree that more transparent communication will build trust overall and, if anything, diminish litigation. We know of no instances so far of lawsuits deriving from open notes.
 

The physician experience

Doctors may worry that open notes will impede workflow, that they will be compelled to “dumb down” their documentation to avoid causing offense or anxiety, and that patients will demand changes to what is written. Here, extensive survey research should allay such fears and expectations. In a survey of more than 1,600 clinicians with at least 1 year of experience with open notes, reports of disruption to workflow were uncommon.

Most doctors (84%) reported that patients contacted them with questions about their notes “less than monthly or never.” Approximately two-thirds (62%) reported spending the same amount of time writing visit notes.

After implementing open notes, many doctors do report being more mindful about their documentation. For example, 41% reported changing how they used language such as “patient denies” or “noncompliant,” and 18% reported changing their use of medical jargon or abbreviations. Might these changes undermine the utility of medical notes? A majority of doctors surveyed (78%) said no, reporting that, after implementing open notes, the value of their documentation was the same or better.

Innovations spotlight difficult and often longstanding challenges. Open notes highlight the complex role of medical records in preserving privacy, especially in the spectrum of abuse, whether domestic or involving elders, children or sexual transgressions. For families with adolescents, issues concerning confidentiality can become a two-way street, and federal and state rules at times provide conflicting and idiosyncratic guidance. It is important to emphasize that the new rules permit information blocking if there is clear evidence that doing so “will substantially reduce the risk of harm” to patients or to other third parties.

Perhaps think of open notes as a new medicine designed to help the vast majority of those who use it but with side effects and even contraindications for a few. Doctors can step in to minimize risks to vulnerable individuals, and imaginative and creative solutions to complex issues may emerge. In a growing number of practices serving adolescents, clinicians can now create two notes, with some elements of care visible on a patient portal and others held privately or visible only to the adolescent.
 

The shared experience

Overall, when it comes to documenting sensitive social information, open notes may act as a useful catalyst prompting deeper discussion about personal details clinically important to record, as opposed to those perhaps best left unwritten.

The implementation of open notes nationwide calls for exciting explorations. How can transparent systems maximize benefits for targeted populations in diverse settings? For patients with mental illness, can notes become part of the therapy? Given that care partners often report more benefit from reading notes than do patients themselves, how can they be mobilized to maximize their contributions to those acutely ill on hospital floors, or to family members with Alzheimer’s or in long-term care facilities?

How can we harness emerging technologies to translate notes and medical records into other languages or support lower literacy levels, while preserving the clinical detail in the notes? Should patients contribute to their own notes, cogenerating them with their clinicians? Experiments for “OurNotes” interventions are underway, and early reports from both patients and doctors hold considerable promise.

Ownership of medical records is evolving. Once firmly held by clinicians, electronic technologies have rapidly led to what may best be viewed currently as joint ownership by clinicians and patients. As apps evolve further and issues with interoperability of records diminish, it is likely that patients will eventually take control. Then it will be up to patients what to carry in their records. Clinicians will advise, but patients will decide.

The new rules herald clear changes in the fabric of care, and after a decade of study we anticipate that the benefits well outweigh the harms. But in the short run, it’s wrong to predict an avalanche. Two decades ago, when patient portals first revealed laboratory test findings to patients, doctors expected cataclysmic change in their practices. It did not occur. The vast majority of patients who registered on portals benefited and few disturbed their doctors.

Similarly, after notes were first unblinded by the OpenNotes research teams, the question we were asked most commonly by the primary care doctors who volunteered was whether the computers were actually displaying their notes. Even though many patients read them carefully, the doctors heard little from them. Clinicians have now reported the same experience in several subsequent studies.

Patients are resourceful, turning quickly to friends or the Internet for answers to their questions. They know how busy doctors are and don’t want to bother them if at all possible. When notes do trigger questions, the time taken to respond is probably offset by silence from other patients finding answers to their own questions in notes they read.

We believe that clinicians should embrace the spirit of the rules and also view them as HIPAA catching up with a computerized universe. As the new practice takes hold, ambiguities will diminish as further experience and research evolve. Warner V. Slack, MD, the first doctor to ask patients to talk to computers, opined that patients are the “largest and least utilized resource in health care.” Open and transparent communication through electronic medical records may mobilize patients (and their families) far more effectively. Patients will almost certainly benefit. Remembering Dr. Slack’s prophecy, we believe that clinicians will too.

A version of this article first appeared on Medscape.com.

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.

Confirmed cases of coronavirus disease 2019 (COVID-19) exceed 111 million, and the disease is responsible for approximately 2.4 million deaths worldwide.¹ In the United States, 28 million cases of COVID-19 have been reported, and the disease has caused more than 497,000 deaths.² The clinical presentation of COVID-19 varies widely, with the most severe presentation characterized by acute respiratory distress syndrome and a marked systemic inflammatory response. Corticosteroids have emerged as a potential therapeutic option in a subset of patients. Results from the recently published RECOVERY trial suggest a substantial mortality benefit of dexamethasone in patients who require mechanical ventilation, with a risk reduction of approximately 33%.³ In addition, a recent large retrospective study demonstrated a reduction in the risk of mechanical ventilation or mortality with corticosteroids in a prespecified subset of patients with C-reactive protein (CRP) ≥20 mg/dL, which indicates a high burden of inflammation.⁴

Some patients with severe COVID-19 experience a positive feedback cascade of proinflammatory cytokines, called the cytokine storm, which can worsen lung injury and, in some cases, progress to vasodilatory shock and multiorgan failure.⁵ This complication’s cytokine cascade includes interleukin (IL) 6, IL-1β, and CC chemokine ligand 3 (CCL3), which are released by airway macrophages and all of which are heavily implicated in the maladaptive forms of immune response to COVID-19.^6,7 The cytokine IL-6 is the primary signal for the production of CRP, and corticosteroids have been shown, both in vitro and in vivo, to reduce the production of IL-6 and other cytokines by airway macrophages.⁶ Levels of CRP have been shown to correlate with outcomes in COVID-19 and bacterial pneumonias.^7,8 Reduction in CRP levels following the institution of therapy, known as CRP response, has been shown to predict outcomes in other inflammatory conditions, such as osteomyelitis, hidradenitis suppurativa, and some cases of bacterial pneumonia.^8-10 Similar CRP response in hemophagocytic lymphohistiocytosis, an entity which closely resembles cytokine storm syndrome, has been shown to correlate with disease activity in patients following treatment with an IL-1 antagonist.¹¹ Whether the CRP response as a response to therapeutics in COVID-19 is associated with improved outcomes remains unknown.

Laboratory measurement of CRP levels offers several advantages over the measurement of interleukins. Notably, the half-life of CRP is approximately 19 hours, which is comparable across different age groups and inflammatory conditions because its concentration depends primarily on synthesis in the liver, and a decreased level suggests decreased stimulus for synthesis.⁸ This makes CRP a useful biomarker to assess response to therapy, in contrast to interleukins, which have short half-lives, are variable in heterogeneous populations, and can be difficult to measure. In addition, CRP measurement is rapid and relatively inexpensive.

We hypothesized that reduction in CRP levels by 50% or more within 72 hours after the initiation of corticosteroids in patients with COVID-19 is associated with reduced inpatient mortality and may be an early indicator of therapeutic response.

Study Participants

In this retrospective cohort study, we reviewed all adult patients admitted to Montefiore Medical Center (Bronx, New York) for COVID-19 between March 10, 2020, and May 2, 2020. Patients must have been discharged (alive or deceased) by the administrative censor date (May 2, 2020) to be included. Patients who died within the first 48 hours of admission were excluded to allow sufficient time for corticosteroid treatment to take effect. For inclusion in the corticosteroid group, patients needed to have received at least 2 consecutive days of corticosteroid treatment beginning within the first 48 hours of admission with a total daily dose of 0.5 mg/kg prednisone equivalent or greater. Patients who received treatment-dose corticosteroids later in the hospital course were excluded (Appendix Figure).

Comparison Group and Outcome

We examined trends in CRP levels for patients who received corticosteroids vs trends among patients who did not receive corticosteroids. In addition, among patients who were treated with corticosteroids, we compared the inpatient mortality of those who did have a reduction in CRP level after treatment with inpatient mortality of those who did not have a reduction in CRP level after treatment. First, CRP level trends over time were examined in all patients, and compared between those who received corticosteroid treatment and those who did not. Then, patients who received corticosteroids were categorized based on changes in CRP levels after beginning corticosteroids. The first CRP level obtained during the first 48 hours of admission was used as the initial CRP level. For each patient, the last CRP level within the 72 hours after initiation of treatment was used to calculate the change in CRP level from admission. A patient was considered to be a “CRP responder” if their CRP level decreased by 50% or more within 72 hours after treatment and a “CRP nonresponder” if their CRP level did not drop by at least 50% within 72 hours of treatment. Patients who did not have a CRP level within the initial 48 hours of admission or a subsequent CRP measured in the 72 hours after treatment were considered to have an “undetermined CRP response” and excluded from the mortality analysis.

We observed a rise in CRP starting around day 6 among patients treated with corticosteroids and performed a post hoc analysis to determine if this was due to a selection effect whereby patients staying in the hospital longer had higher CRP levels or represented actual rise. In order to address this, we performed a stratified analysis comparing the trends in CRP levels among patients with a length of stay (LOS) of 7 or more days with trends among those with an LOS less than 7 days.

Statistical Analysis

To characterize differences in patients who received corticosteroids and those who did not, we examined their demographic, clinical characteristics, and admission laboratory values, using chi-square test for categorical variables and Kruskal-Wallis test for continuous variables (Table 1). The change in CRP levels from day 0 (presentation to the hospital) in both groups was plotted in a time-series analysis. For each day in the time series, the 95% CIs for the changes in CRP were computed using the t statistic for the corresponding distribution. The Kruskal-Wallis test was used to assess the significance of differences between groups at 72 hours after initiation of treatment.

After categorizing patients by CRP response, we compared demographic, clinical, and laboratory characteristics of patients who were CRP responsive with those of patients who were not, using the same tests of statistical inference mentioned above. To compare time to inpatient mortality differences between CRP response groups, Kaplan-Meier survival curves were generated and statistical significance determined via log-rank test. Univariable logistic regression was used to estimate the odds ratio of inpatient mortality between comparison groups in an unadjusted analysis. Last, to examine the independent association between CRP response and mortality, we constructed a multivariate model that included variables that were significantly associated with mortality in univariable analysis and considered to be important potential confounders by the authors. Details on variable selection for the model are listed in Appendix Table 1.

Data Collection

Data were directly extracted from our center’s electronic health record system. Data processing and recoding was performed using the Python programming language (version 2.7.17) and data analysis was done using Stata 12 (StataCorp LLC; 2011). This study was approved by the institutional review board of the Albert Einstein College of Medicine.

Corticosteroids vs No Corticosteroids

Between March 10, 2020, and May 2, 2020, a total of 3,382 adult patients were admitted for COVID-19 at Montefiore Medical Center. Of these, 2,707 patients met the study inclusion criteria, and 324 of those received corticosteroid treatment. Their demographic characteristics, comorbidities, and admission lab values are shown in Table 1. Patients who received corticosteroids were older, had higher comorbidity scores, were more likely to have asthma or chronic obstructive pulmonary disease, and were less likely to be full code status, compared with patients who did not receive corticosteroids. Patients who received corticosteroids also had higher initial white blood cell (WBC) and neutrophil counts but lower lymphocyte count. The two groups were comparable in initial creatinine level. Additional patient characteristics and addmission lab values are shown in Appendix Table 2.

Average change in CRP levels by hospital day for those who received corticosteroids and those who did not are shown in Figure 1A. Among patients who received corticosteroid treatment, there was a significant decrease in CRP level at 72 hours of treatment (P < .001). In the post hoc analysis of trends in CRP levels, we found that CRP levels among those treated with corticosteroids started to rise around day 6 after the initial drop. This trend was observed even after removing patients with shorter LOS (<7 days) (Figure 1B). The median durations of corticosteroid therapy were 3 days among patients whose LOS was less than 7 days and 6 days among those whose LOS was 7 days or greater. The rise in CRP level was seen at day 5 and day 7 within each group, respectively. Crude death rate was 41.7% among patients with LOS of less than 7 days and 40.6% in those with LOS of 7 days or greater.

CRP Responders vs Nonresponders

Among the 324 patients who received corticosteroids, 131 (40.4%) were classified as responders, 92 (28.4%) were classified as nonresponders, and 101 (31.2%) were undetermined. Characteristics of CRP responders and CRP nonresponders are shown in Table 2 and Appendix Table 3. CRP responders were more likely to have dementia, higher median admission platelet count, and fibrinogen level compared with CRP nonresponders. Patients whose CRP response was undetermined were excluded from the analysis. Their characteristics are shown in Appendix Table 4.

The observed inpatient mortality rate was 25.2% among CRP responders and 47.8% among CRP nonresponders. This was also demonstrated in the Kaplan-Meier survival curve (Figure 2). The odds of inpatient mortality among CRP responders was strongly and significantly reduced compared with those among nonresponders in an unadjusted analysis (odds ratio [OR], 0.37; 95% CI, 0.21-0.65; P = .001) and after adjustment for demographic and clinical characteristics including age, Charlson Comorbidity Index, initial WBC count, initial CRP level, and initial fibrinogen level (OR, 0.27; 95% CI, 0.14-0.54; P < .001). Details on how variables were operationalized and information on missing data are included in Appendix Table 1.

To explore whether this observed effect differed depending on severity of the respiratory illness, we examined the association between CRP response and mortality in subgroups stratified by intubation status. Within our cohort of 223 patients (92 CRP responders and 131 CRP nonresponders), 166 patients were never intubated, 50 patients were intubated in the first 48 hours, and 7 patients were intubated later on during the admission. The odds ratios for death among CRP responders vs nonresponders were 0.50 (P = .07) among patients never intubated and 0.46 (P = .2) among patients intubated within the initial 48 hours of admission.

In this retrospective study, we found that, on average, patients treated with corticosteroids had a swift and marked reduction in serum CRP. In addition, among patients treated with corticosteroids, those whose CRP was reduced by 50% or more within 72 hours after treatment had a dramatically reduced risk of inpatient mortality compared with the risk among nonresponders. This study contributes to a growing body of evidence that suggests that corticosteroids may be an efficacious treatment to reduce adverse events in patients with COVID-19 who have evidence of high levels of inflammation as measured by CRP level.^3,4,12,13

It remains unclear whether CRP is simply a biomarker of disease activity or if it plays a role in mediating inflammation. While CRP is commonly understood to be an acute phase reactant, it has been suggested that, after undergoing proteolysis, it functions as a chemoattractant for monocytes.¹⁴ In addition, it is now known that the inflammatory CD14+/CD16+ monocytes that express high levels of IL-6 are key drivers of the cytokine storm in COVID-19.¹⁵ Therefore, it may be possible that the high levels of circulating CRP in patients with cytokine storm recruits monocytes to the lungs, which leads to further lung injury.

Other mechanisms of immune dysregulation that may contribute to lung injury and respiratory failure in COVID-19, such as cytokine-induced T-cell suppression, have been proposed.^7,16 The related markers, such as levels of T-cells or specific cytokines, may therefore represent different but related underlying immune mechanisms affecting the clinical course of COVID-19 that may respond to different therapeutic modalities such as direct IL-6 blockade or chemokine receptor blockade, among others that are currently under investigation.^17,18

Regardless of the underlying mechanism of immune regulation, our study shows that serial measurement of CRP may serve as an early indicator of response to corticosteroids that correlates with decreased mortality. The association between CRP response and reduced risk of mortality was present in both subgroups, those requiring mechanical ventilation and those who did not. The risk reduction was similar in magnitude to the overall effect but was not statistically significant in either group. Interestingly, our time series analysis demonstrated a rise in CRP around day 6 among patients treated with corticosteroids (notably, most patients were treated for 5 to 7 days). Our post hoc analysis suggests that this may represent a “rebound” in inflammation after discontinuation of corticosteroids. However, the clinical significance of this rebound and whether a longer course of steroids would improve outcomes is not known. Because corticosteroid therapy may be associated with adverse effects in some patients,⁴ it is possible that CRP nonresponders represent a subset of patients in whom corticosteroids are not effective and for whom alternative therapies should be considered. In one study looking at the usefulness of IL-1 inhibition for severe COVID-19 infection, patients who received IL-1 inhibitor therapy had improved mortality and a significant decrease in CRP concentration as compared with the historical group.¹⁹ Finally, it is worth noting that, in one large retrospective study, there was harm associated with corticosteroid therapy in patients with low levels of CRP, and in the RECOVERY trial there was a trend toward harm for patients with no oxygen requirement.^3,4 Serial measurement of CRP may further identify the subset of patients in whom corticosteroid therapy might be harmful.

This study has several limitations. First, the retrospective nature of this study is inherently prone to selection bias, and despite the large number of clinical variables accounted for, unmeasured confounders may still exist. This study was also conducted at a single clinical center operating under emergency circumstances at a time during which healthcare resources were limited. Overall in-hospital mortality was high but similar to mortality rates reported at other hospitals in the New York City area during the same months.²⁰ The strengths of this study include a large cohort of COVID-19 patients from New York City, an epicenter of COVID-19, who received corticosteroids.

We found that therapy with corticosteroids in patients with COVID-19 is associated with a substantial reduction in CRP levels within 72 hours of therapy, and for those patients in whom CRP levels decrease by 50% or more, there is a significantly lower risk of inpatient mortality. Future studies are needed to validate these findings in other cohorts and to determine if markers other than CRP levels may be predictors of a therapeutic response or if CRP nonresponders would benefit from other targeted therapies.

Confirmed cases of coronavirus disease 2019 (COVID-19) exceed 111 million, and the disease is responsible for approximately 2.4 million deaths worldwide.¹ In the United States, 28 million cases of COVID-19 have been reported, and the disease has caused more than 497,000 deaths.² The clinical presentation of COVID-19 varies widely, with the most severe presentation characterized by acute respiratory distress syndrome and a marked systemic inflammatory response. Corticosteroids have emerged as a potential therapeutic option in a subset of patients. Results from the recently published RECOVERY trial suggest a substantial mortality benefit of dexamethasone in patients who require mechanical ventilation, with a risk reduction of approximately 33%.³ In addition, a recent large retrospective study demonstrated a reduction in the risk of mechanical ventilation or mortality with corticosteroids in a prespecified subset of patients with C-reactive protein (CRP) ≥20 mg/dL, which indicates a high burden of inflammation.⁴

Some patients with severe COVID-19 experience a positive feedback cascade of proinflammatory cytokines, called the cytokine storm, which can worsen lung injury and, in some cases, progress to vasodilatory shock and multiorgan failure.⁵ This complication’s cytokine cascade includes interleukin (IL) 6, IL-1β, and CC chemokine ligand 3 (CCL3), which are released by airway macrophages and all of which are heavily implicated in the maladaptive forms of immune response to COVID-19.^6,7 The cytokine IL-6 is the primary signal for the production of CRP, and corticosteroids have been shown, both in vitro and in vivo, to reduce the production of IL-6 and other cytokines by airway macrophages.⁶ Levels of CRP have been shown to correlate with outcomes in COVID-19 and bacterial pneumonias.^7,8 Reduction in CRP levels following the institution of therapy, known as CRP response, has been shown to predict outcomes in other inflammatory conditions, such as osteomyelitis, hidradenitis suppurativa, and some cases of bacterial pneumonia.^8-10 Similar CRP response in hemophagocytic lymphohistiocytosis, an entity which closely resembles cytokine storm syndrome, has been shown to correlate with disease activity in patients following treatment with an IL-1 antagonist.¹¹ Whether the CRP response as a response to therapeutics in COVID-19 is associated with improved outcomes remains unknown.

Laboratory measurement of CRP levels offers several advantages over the measurement of interleukins. Notably, the half-life of CRP is approximately 19 hours, which is comparable across different age groups and inflammatory conditions because its concentration depends primarily on synthesis in the liver, and a decreased level suggests decreased stimulus for synthesis.⁸ This makes CRP a useful biomarker to assess response to therapy, in contrast to interleukins, which have short half-lives, are variable in heterogeneous populations, and can be difficult to measure. In addition, CRP measurement is rapid and relatively inexpensive.

We hypothesized that reduction in CRP levels by 50% or more within 72 hours after the initiation of corticosteroids in patients with COVID-19 is associated with reduced inpatient mortality and may be an early indicator of therapeutic response.

Study Participants

In this retrospective cohort study, we reviewed all adult patients admitted to Montefiore Medical Center (Bronx, New York) for COVID-19 between March 10, 2020, and May 2, 2020. Patients must have been discharged (alive or deceased) by the administrative censor date (May 2, 2020) to be included. Patients who died within the first 48 hours of admission were excluded to allow sufficient time for corticosteroid treatment to take effect. For inclusion in the corticosteroid group, patients needed to have received at least 2 consecutive days of corticosteroid treatment beginning within the first 48 hours of admission with a total daily dose of 0.5 mg/kg prednisone equivalent or greater. Patients who received treatment-dose corticosteroids later in the hospital course were excluded (Appendix Figure).

Comparison Group and Outcome

We examined trends in CRP levels for patients who received corticosteroids vs trends among patients who did not receive corticosteroids. In addition, among patients who were treated with corticosteroids, we compared the inpatient mortality of those who did have a reduction in CRP level after treatment with inpatient mortality of those who did not have a reduction in CRP level after treatment. First, CRP level trends over time were examined in all patients, and compared between those who received corticosteroid treatment and those who did not. Then, patients who received corticosteroids were categorized based on changes in CRP levels after beginning corticosteroids. The first CRP level obtained during the first 48 hours of admission was used as the initial CRP level. For each patient, the last CRP level within the 72 hours after initiation of treatment was used to calculate the change in CRP level from admission. A patient was considered to be a “CRP responder” if their CRP level decreased by 50% or more within 72 hours after treatment and a “CRP nonresponder” if their CRP level did not drop by at least 50% within 72 hours of treatment. Patients who did not have a CRP level within the initial 48 hours of admission or a subsequent CRP measured in the 72 hours after treatment were considered to have an “undetermined CRP response” and excluded from the mortality analysis.

We observed a rise in CRP starting around day 6 among patients treated with corticosteroids and performed a post hoc analysis to determine if this was due to a selection effect whereby patients staying in the hospital longer had higher CRP levels or represented actual rise. In order to address this, we performed a stratified analysis comparing the trends in CRP levels among patients with a length of stay (LOS) of 7 or more days with trends among those with an LOS less than 7 days.

Statistical Analysis

To characterize differences in patients who received corticosteroids and those who did not, we examined their demographic, clinical characteristics, and admission laboratory values, using chi-square test for categorical variables and Kruskal-Wallis test for continuous variables (Table 1). The change in CRP levels from day 0 (presentation to the hospital) in both groups was plotted in a time-series analysis. For each day in the time series, the 95% CIs for the changes in CRP were computed using the t statistic for the corresponding distribution. The Kruskal-Wallis test was used to assess the significance of differences between groups at 72 hours after initiation of treatment.

After categorizing patients by CRP response, we compared demographic, clinical, and laboratory characteristics of patients who were CRP responsive with those of patients who were not, using the same tests of statistical inference mentioned above. To compare time to inpatient mortality differences between CRP response groups, Kaplan-Meier survival curves were generated and statistical significance determined via log-rank test. Univariable logistic regression was used to estimate the odds ratio of inpatient mortality between comparison groups in an unadjusted analysis. Last, to examine the independent association between CRP response and mortality, we constructed a multivariate model that included variables that were significantly associated with mortality in univariable analysis and considered to be important potential confounders by the authors. Details on variable selection for the model are listed in Appendix Table 1.

Data Collection

Data were directly extracted from our center’s electronic health record system. Data processing and recoding was performed using the Python programming language (version 2.7.17) and data analysis was done using Stata 12 (StataCorp LLC; 2011). This study was approved by the institutional review board of the Albert Einstein College of Medicine.

Corticosteroids vs No Corticosteroids

Between March 10, 2020, and May 2, 2020, a total of 3,382 adult patients were admitted for COVID-19 at Montefiore Medical Center. Of these, 2,707 patients met the study inclusion criteria, and 324 of those received corticosteroid treatment. Their demographic characteristics, comorbidities, and admission lab values are shown in Table 1. Patients who received corticosteroids were older, had higher comorbidity scores, were more likely to have asthma or chronic obstructive pulmonary disease, and were less likely to be full code status, compared with patients who did not receive corticosteroids. Patients who received corticosteroids also had higher initial white blood cell (WBC) and neutrophil counts but lower lymphocyte count. The two groups were comparable in initial creatinine level. Additional patient characteristics and addmission lab values are shown in Appendix Table 2.

Average change in CRP levels by hospital day for those who received corticosteroids and those who did not are shown in Figure 1A. Among patients who received corticosteroid treatment, there was a significant decrease in CRP level at 72 hours of treatment (P < .001). In the post hoc analysis of trends in CRP levels, we found that CRP levels among those treated with corticosteroids started to rise around day 6 after the initial drop. This trend was observed even after removing patients with shorter LOS (<7 days) (Figure 1B). The median durations of corticosteroid therapy were 3 days among patients whose LOS was less than 7 days and 6 days among those whose LOS was 7 days or greater. The rise in CRP level was seen at day 5 and day 7 within each group, respectively. Crude death rate was 41.7% among patients with LOS of less than 7 days and 40.6% in those with LOS of 7 days or greater.

CRP Responders vs Nonresponders

Among the 324 patients who received corticosteroids, 131 (40.4%) were classified as responders, 92 (28.4%) were classified as nonresponders, and 101 (31.2%) were undetermined. Characteristics of CRP responders and CRP nonresponders are shown in Table 2 and Appendix Table 3. CRP responders were more likely to have dementia, higher median admission platelet count, and fibrinogen level compared with CRP nonresponders. Patients whose CRP response was undetermined were excluded from the analysis. Their characteristics are shown in Appendix Table 4.

The observed inpatient mortality rate was 25.2% among CRP responders and 47.8% among CRP nonresponders. This was also demonstrated in the Kaplan-Meier survival curve (Figure 2). The odds of inpatient mortality among CRP responders was strongly and significantly reduced compared with those among nonresponders in an unadjusted analysis (odds ratio [OR], 0.37; 95% CI, 0.21-0.65; P = .001) and after adjustment for demographic and clinical characteristics including age, Charlson Comorbidity Index, initial WBC count, initial CRP level, and initial fibrinogen level (OR, 0.27; 95% CI, 0.14-0.54; P < .001). Details on how variables were operationalized and information on missing data are included in Appendix Table 1.

To explore whether this observed effect differed depending on severity of the respiratory illness, we examined the association between CRP response and mortality in subgroups stratified by intubation status. Within our cohort of 223 patients (92 CRP responders and 131 CRP nonresponders), 166 patients were never intubated, 50 patients were intubated in the first 48 hours, and 7 patients were intubated later on during the admission. The odds ratios for death among CRP responders vs nonresponders were 0.50 (P = .07) among patients never intubated and 0.46 (P = .2) among patients intubated within the initial 48 hours of admission.

In this retrospective study, we found that, on average, patients treated with corticosteroids had a swift and marked reduction in serum CRP. In addition, among patients treated with corticosteroids, those whose CRP was reduced by 50% or more within 72 hours after treatment had a dramatically reduced risk of inpatient mortality compared with the risk among nonresponders. This study contributes to a growing body of evidence that suggests that corticosteroids may be an efficacious treatment to reduce adverse events in patients with COVID-19 who have evidence of high levels of inflammation as measured by CRP level.^3,4,12,13

It remains unclear whether CRP is simply a biomarker of disease activity or if it plays a role in mediating inflammation. While CRP is commonly understood to be an acute phase reactant, it has been suggested that, after undergoing proteolysis, it functions as a chemoattractant for monocytes.¹⁴ In addition, it is now known that the inflammatory CD14+/CD16+ monocytes that express high levels of IL-6 are key drivers of the cytokine storm in COVID-19.¹⁵ Therefore, it may be possible that the high levels of circulating CRP in patients with cytokine storm recruits monocytes to the lungs, which leads to further lung injury.

Other mechanisms of immune dysregulation that may contribute to lung injury and respiratory failure in COVID-19, such as cytokine-induced T-cell suppression, have been proposed.^7,16 The related markers, such as levels of T-cells or specific cytokines, may therefore represent different but related underlying immune mechanisms affecting the clinical course of COVID-19 that may respond to different therapeutic modalities such as direct IL-6 blockade or chemokine receptor blockade, among others that are currently under investigation.^17,18

Regardless of the underlying mechanism of immune regulation, our study shows that serial measurement of CRP may serve as an early indicator of response to corticosteroids that correlates with decreased mortality. The association between CRP response and reduced risk of mortality was present in both subgroups, those requiring mechanical ventilation and those who did not. The risk reduction was similar in magnitude to the overall effect but was not statistically significant in either group. Interestingly, our time series analysis demonstrated a rise in CRP around day 6 among patients treated with corticosteroids (notably, most patients were treated for 5 to 7 days). Our post hoc analysis suggests that this may represent a “rebound” in inflammation after discontinuation of corticosteroids. However, the clinical significance of this rebound and whether a longer course of steroids would improve outcomes is not known. Because corticosteroid therapy may be associated with adverse effects in some patients,⁴ it is possible that CRP nonresponders represent a subset of patients in whom corticosteroids are not effective and for whom alternative therapies should be considered. In one study looking at the usefulness of IL-1 inhibition for severe COVID-19 infection, patients who received IL-1 inhibitor therapy had improved mortality and a significant decrease in CRP concentration as compared with the historical group.¹⁹ Finally, it is worth noting that, in one large retrospective study, there was harm associated with corticosteroid therapy in patients with low levels of CRP, and in the RECOVERY trial there was a trend toward harm for patients with no oxygen requirement.^3,4 Serial measurement of CRP may further identify the subset of patients in whom corticosteroid therapy might be harmful.

This study has several limitations. First, the retrospective nature of this study is inherently prone to selection bias, and despite the large number of clinical variables accounted for, unmeasured confounders may still exist. This study was also conducted at a single clinical center operating under emergency circumstances at a time during which healthcare resources were limited. Overall in-hospital mortality was high but similar to mortality rates reported at other hospitals in the New York City area during the same months.²⁰ The strengths of this study include a large cohort of COVID-19 patients from New York City, an epicenter of COVID-19, who received corticosteroids.

We found that therapy with corticosteroids in patients with COVID-19 is associated with a substantial reduction in CRP levels within 72 hours of therapy, and for those patients in whom CRP levels decrease by 50% or more, there is a significantly lower risk of inpatient mortality. Future studies are needed to validate these findings in other cohorts and to determine if markers other than CRP levels may be predictors of a therapeutic response or if CRP nonresponders would benefit from other targeted therapies.

Confirmed cases of coronavirus disease 2019 (COVID-19) exceed 111 million, and the disease is responsible for approximately 2.4 million deaths worldwide.¹ In the United States, 28 million cases of COVID-19 have been reported, and the disease has caused more than 497,000 deaths.² The clinical presentation of COVID-19 varies widely, with the most severe presentation characterized by acute respiratory distress syndrome and a marked systemic inflammatory response. Corticosteroids have emerged as a potential therapeutic option in a subset of patients. Results from the recently published RECOVERY trial suggest a substantial mortality benefit of dexamethasone in patients who require mechanical ventilation, with a risk reduction of approximately 33%.³ In addition, a recent large retrospective study demonstrated a reduction in the risk of mechanical ventilation or mortality with corticosteroids in a prespecified subset of patients with C-reactive protein (CRP) ≥20 mg/dL, which indicates a high burden of inflammation.⁴

Some patients with severe COVID-19 experience a positive feedback cascade of proinflammatory cytokines, called the cytokine storm, which can worsen lung injury and, in some cases, progress to vasodilatory shock and multiorgan failure.⁵ This complication’s cytokine cascade includes interleukin (IL) 6, IL-1β, and CC chemokine ligand 3 (CCL3), which are released by airway macrophages and all of which are heavily implicated in the maladaptive forms of immune response to COVID-19.^6,7 The cytokine IL-6 is the primary signal for the production of CRP, and corticosteroids have been shown, both in vitro and in vivo, to reduce the production of IL-6 and other cytokines by airway macrophages.⁶ Levels of CRP have been shown to correlate with outcomes in COVID-19 and bacterial pneumonias.^7,8 Reduction in CRP levels following the institution of therapy, known as CRP response, has been shown to predict outcomes in other inflammatory conditions, such as osteomyelitis, hidradenitis suppurativa, and some cases of bacterial pneumonia.^8-10 Similar CRP response in hemophagocytic lymphohistiocytosis, an entity which closely resembles cytokine storm syndrome, has been shown to correlate with disease activity in patients following treatment with an IL-1 antagonist.¹¹ Whether the CRP response as a response to therapeutics in COVID-19 is associated with improved outcomes remains unknown.

Laboratory measurement of CRP levels offers several advantages over the measurement of interleukins. Notably, the half-life of CRP is approximately 19 hours, which is comparable across different age groups and inflammatory conditions because its concentration depends primarily on synthesis in the liver, and a decreased level suggests decreased stimulus for synthesis.⁸ This makes CRP a useful biomarker to assess response to therapy, in contrast to interleukins, which have short half-lives, are variable in heterogeneous populations, and can be difficult to measure. In addition, CRP measurement is rapid and relatively inexpensive.

We hypothesized that reduction in CRP levels by 50% or more within 72 hours after the initiation of corticosteroids in patients with COVID-19 is associated with reduced inpatient mortality and may be an early indicator of therapeutic response.

Study Participants

In this retrospective cohort study, we reviewed all adult patients admitted to Montefiore Medical Center (Bronx, New York) for COVID-19 between March 10, 2020, and May 2, 2020. Patients must have been discharged (alive or deceased) by the administrative censor date (May 2, 2020) to be included. Patients who died within the first 48 hours of admission were excluded to allow sufficient time for corticosteroid treatment to take effect. For inclusion in the corticosteroid group, patients needed to have received at least 2 consecutive days of corticosteroid treatment beginning within the first 48 hours of admission with a total daily dose of 0.5 mg/kg prednisone equivalent or greater. Patients who received treatment-dose corticosteroids later in the hospital course were excluded (Appendix Figure).

Comparison Group and Outcome

We examined trends in CRP levels for patients who received corticosteroids vs trends among patients who did not receive corticosteroids. In addition, among patients who were treated with corticosteroids, we compared the inpatient mortality of those who did have a reduction in CRP level after treatment with inpatient mortality of those who did not have a reduction in CRP level after treatment. First, CRP level trends over time were examined in all patients, and compared between those who received corticosteroid treatment and those who did not. Then, patients who received corticosteroids were categorized based on changes in CRP levels after beginning corticosteroids. The first CRP level obtained during the first 48 hours of admission was used as the initial CRP level. For each patient, the last CRP level within the 72 hours after initiation of treatment was used to calculate the change in CRP level from admission. A patient was considered to be a “CRP responder” if their CRP level decreased by 50% or more within 72 hours after treatment and a “CRP nonresponder” if their CRP level did not drop by at least 50% within 72 hours of treatment. Patients who did not have a CRP level within the initial 48 hours of admission or a subsequent CRP measured in the 72 hours after treatment were considered to have an “undetermined CRP response” and excluded from the mortality analysis.

We observed a rise in CRP starting around day 6 among patients treated with corticosteroids and performed a post hoc analysis to determine if this was due to a selection effect whereby patients staying in the hospital longer had higher CRP levels or represented actual rise. In order to address this, we performed a stratified analysis comparing the trends in CRP levels among patients with a length of stay (LOS) of 7 or more days with trends among those with an LOS less than 7 days.

Statistical Analysis

To characterize differences in patients who received corticosteroids and those who did not, we examined their demographic, clinical characteristics, and admission laboratory values, using chi-square test for categorical variables and Kruskal-Wallis test for continuous variables (Table 1). The change in CRP levels from day 0 (presentation to the hospital) in both groups was plotted in a time-series analysis. For each day in the time series, the 95% CIs for the changes in CRP were computed using the t statistic for the corresponding distribution. The Kruskal-Wallis test was used to assess the significance of differences between groups at 72 hours after initiation of treatment.

After categorizing patients by CRP response, we compared demographic, clinical, and laboratory characteristics of patients who were CRP responsive with those of patients who were not, using the same tests of statistical inference mentioned above. To compare time to inpatient mortality differences between CRP response groups, Kaplan-Meier survival curves were generated and statistical significance determined via log-rank test. Univariable logistic regression was used to estimate the odds ratio of inpatient mortality between comparison groups in an unadjusted analysis. Last, to examine the independent association between CRP response and mortality, we constructed a multivariate model that included variables that were significantly associated with mortality in univariable analysis and considered to be important potential confounders by the authors. Details on variable selection for the model are listed in Appendix Table 1.

Data Collection

Data were directly extracted from our center’s electronic health record system. Data processing and recoding was performed using the Python programming language (version 2.7.17) and data analysis was done using Stata 12 (StataCorp LLC; 2011). This study was approved by the institutional review board of the Albert Einstein College of Medicine.

Corticosteroids vs No Corticosteroids

Between March 10, 2020, and May 2, 2020, a total of 3,382 adult patients were admitted for COVID-19 at Montefiore Medical Center. Of these, 2,707 patients met the study inclusion criteria, and 324 of those received corticosteroid treatment. Their demographic characteristics, comorbidities, and admission lab values are shown in Table 1. Patients who received corticosteroids were older, had higher comorbidity scores, were more likely to have asthma or chronic obstructive pulmonary disease, and were less likely to be full code status, compared with patients who did not receive corticosteroids. Patients who received corticosteroids also had higher initial white blood cell (WBC) and neutrophil counts but lower lymphocyte count. The two groups were comparable in initial creatinine level. Additional patient characteristics and addmission lab values are shown in Appendix Table 2.

Average change in CRP levels by hospital day for those who received corticosteroids and those who did not are shown in Figure 1A. Among patients who received corticosteroid treatment, there was a significant decrease in CRP level at 72 hours of treatment (P < .001). In the post hoc analysis of trends in CRP levels, we found that CRP levels among those treated with corticosteroids started to rise around day 6 after the initial drop. This trend was observed even after removing patients with shorter LOS (<7 days) (Figure 1B). The median durations of corticosteroid therapy were 3 days among patients whose LOS was less than 7 days and 6 days among those whose LOS was 7 days or greater. The rise in CRP level was seen at day 5 and day 7 within each group, respectively. Crude death rate was 41.7% among patients with LOS of less than 7 days and 40.6% in those with LOS of 7 days or greater.

CRP Responders vs Nonresponders

Among the 324 patients who received corticosteroids, 131 (40.4%) were classified as responders, 92 (28.4%) were classified as nonresponders, and 101 (31.2%) were undetermined. Characteristics of CRP responders and CRP nonresponders are shown in Table 2 and Appendix Table 3. CRP responders were more likely to have dementia, higher median admission platelet count, and fibrinogen level compared with CRP nonresponders. Patients whose CRP response was undetermined were excluded from the analysis. Their characteristics are shown in Appendix Table 4.

The observed inpatient mortality rate was 25.2% among CRP responders and 47.8% among CRP nonresponders. This was also demonstrated in the Kaplan-Meier survival curve (Figure 2). The odds of inpatient mortality among CRP responders was strongly and significantly reduced compared with those among nonresponders in an unadjusted analysis (odds ratio [OR], 0.37; 95% CI, 0.21-0.65; P = .001) and after adjustment for demographic and clinical characteristics including age, Charlson Comorbidity Index, initial WBC count, initial CRP level, and initial fibrinogen level (OR, 0.27; 95% CI, 0.14-0.54; P < .001). Details on how variables were operationalized and information on missing data are included in Appendix Table 1.

To explore whether this observed effect differed depending on severity of the respiratory illness, we examined the association between CRP response and mortality in subgroups stratified by intubation status. Within our cohort of 223 patients (92 CRP responders and 131 CRP nonresponders), 166 patients were never intubated, 50 patients were intubated in the first 48 hours, and 7 patients were intubated later on during the admission. The odds ratios for death among CRP responders vs nonresponders were 0.50 (P = .07) among patients never intubated and 0.46 (P = .2) among patients intubated within the initial 48 hours of admission.

In this retrospective study, we found that, on average, patients treated with corticosteroids had a swift and marked reduction in serum CRP. In addition, among patients treated with corticosteroids, those whose CRP was reduced by 50% or more within 72 hours after treatment had a dramatically reduced risk of inpatient mortality compared with the risk among nonresponders. This study contributes to a growing body of evidence that suggests that corticosteroids may be an efficacious treatment to reduce adverse events in patients with COVID-19 who have evidence of high levels of inflammation as measured by CRP level.^3,4,12,13

It remains unclear whether CRP is simply a biomarker of disease activity or if it plays a role in mediating inflammation. While CRP is commonly understood to be an acute phase reactant, it has been suggested that, after undergoing proteolysis, it functions as a chemoattractant for monocytes.¹⁴ In addition, it is now known that the inflammatory CD14+/CD16+ monocytes that express high levels of IL-6 are key drivers of the cytokine storm in COVID-19.¹⁵ Therefore, it may be possible that the high levels of circulating CRP in patients with cytokine storm recruits monocytes to the lungs, which leads to further lung injury.

Other mechanisms of immune dysregulation that may contribute to lung injury and respiratory failure in COVID-19, such as cytokine-induced T-cell suppression, have been proposed.^7,16 The related markers, such as levels of T-cells or specific cytokines, may therefore represent different but related underlying immune mechanisms affecting the clinical course of COVID-19 that may respond to different therapeutic modalities such as direct IL-6 blockade or chemokine receptor blockade, among others that are currently under investigation.^17,18

Regardless of the underlying mechanism of immune regulation, our study shows that serial measurement of CRP may serve as an early indicator of response to corticosteroids that correlates with decreased mortality. The association between CRP response and reduced risk of mortality was present in both subgroups, those requiring mechanical ventilation and those who did not. The risk reduction was similar in magnitude to the overall effect but was not statistically significant in either group. Interestingly, our time series analysis demonstrated a rise in CRP around day 6 among patients treated with corticosteroids (notably, most patients were treated for 5 to 7 days). Our post hoc analysis suggests that this may represent a “rebound” in inflammation after discontinuation of corticosteroids. However, the clinical significance of this rebound and whether a longer course of steroids would improve outcomes is not known. Because corticosteroid therapy may be associated with adverse effects in some patients,⁴ it is possible that CRP nonresponders represent a subset of patients in whom corticosteroids are not effective and for whom alternative therapies should be considered. In one study looking at the usefulness of IL-1 inhibition for severe COVID-19 infection, patients who received IL-1 inhibitor therapy had improved mortality and a significant decrease in CRP concentration as compared with the historical group.¹⁹ Finally, it is worth noting that, in one large retrospective study, there was harm associated with corticosteroid therapy in patients with low levels of CRP, and in the RECOVERY trial there was a trend toward harm for patients with no oxygen requirement.^3,4 Serial measurement of CRP may further identify the subset of patients in whom corticosteroid therapy might be harmful.

This study has several limitations. First, the retrospective nature of this study is inherently prone to selection bias, and despite the large number of clinical variables accounted for, unmeasured confounders may still exist. This study was also conducted at a single clinical center operating under emergency circumstances at a time during which healthcare resources were limited. Overall in-hospital mortality was high but similar to mortality rates reported at other hospitals in the New York City area during the same months.²⁰ The strengths of this study include a large cohort of COVID-19 patients from New York City, an epicenter of COVID-19, who received corticosteroids.

We found that therapy with corticosteroids in patients with COVID-19 is associated with a substantial reduction in CRP levels within 72 hours of therapy, and for those patients in whom CRP levels decrease by 50% or more, there is a significantly lower risk of inpatient mortality. Future studies are needed to validate these findings in other cohorts and to determine if markers other than CRP levels may be predictors of a therapeutic response or if CRP nonresponders would benefit from other targeted therapies.

Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)¹ throughout nearly all aspects of academic medicine, including rank,^2-4 tenure,⁵ authorship,^6,7 funding opportunities,^8,9 awards,¹⁰ speakership,¹¹ leadership,^12,13 and salaries.^2,14,15 Hospital medicine, despite being a newer field,¹⁶ has also seen these disparities^17,18; however, there are numerous efforts in place to actively change our specialty’s course.^19-22 Hospital medicine is a field known for being a change agent in healthcare delivery,²² and its novel approaches are well poised to fundamentally shatter the glass ceilings imposed on traditionally underrepresented groups in medicine. The importance of diversity, equity, and inclusion (DEI) initiatives in healthcare has never been clearer,^23,24 particularly as they relate to cultural competence^25-28 and cultural humility,^29,30 implicit and explicit bias,²⁷ expanding care for underserved patient populations, supporting our workforce, and broadening research agendas.²⁸

In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.

Our Division’s Framework to DEI—“It Takes a Village”

Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues. Key areas of focus included institutional structures, our people, our environments, and our core missions (Figure 1 and Appendix Figure 1). DHM members helped drive our work and partnered with departmental, hospital, and school of medicine committees; national organizations; and collaborators to enhance implementation and dissemination efforts. In addition to stakeholder engagement, we utilized strategic planning and rapid Plan-Do-Study-Act (PDSA) cycles to advance DEI work in our DHM.

Needs Assessment

As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.

Interventions

TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.

Our institutional structures

Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to compensation,³¹ recruitment,³² and policies that support and foster a culture of DEI.³² These strategies were used to support the following initiatives:

Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks³³ for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.

A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.

Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.³² Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.

Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.

Our People

The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.

Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.

Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.

Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.

Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. Additionally, our team members conducted environmental scans (eg, identified pictures, artwork, or other images that were not representative of a diverse and inclusive environment and raised concerns when the environment was not inclusive).

Measures

Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.

Analysis

Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.

Strategic Plan Development and Tracking

From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.

Compensation

One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.

Recruitment and Advancement

Toolkits³² and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.

Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.

We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.

Environment

We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.

Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.

We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.

DISCUSSION

The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.

We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.

By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.³⁴ By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.

Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs³⁵) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking³³ as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.

Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.

We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:

• Instituting implicit bias training for all of our faculty
• Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
• Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
• Completion of a diversity dashboard to track our progress in all of these efforts over time
• Development of a more robust pipeline to promotion and leadership for our URM faculty

This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.

Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.

Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)¹ throughout nearly all aspects of academic medicine, including rank,^2-4 tenure,⁵ authorship,^6,7 funding opportunities,^8,9 awards,¹⁰ speakership,¹¹ leadership,^12,13 and salaries.^2,14,15 Hospital medicine, despite being a newer field,¹⁶ has also seen these disparities^17,18; however, there are numerous efforts in place to actively change our specialty’s course.^19-22 Hospital medicine is a field known for being a change agent in healthcare delivery,²² and its novel approaches are well poised to fundamentally shatter the glass ceilings imposed on traditionally underrepresented groups in medicine. The importance of diversity, equity, and inclusion (DEI) initiatives in healthcare has never been clearer,^23,24 particularly as they relate to cultural competence^25-28 and cultural humility,^29,30 implicit and explicit bias,²⁷ expanding care for underserved patient populations, supporting our workforce, and broadening research agendas.²⁸

In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.

Our Division’s Framework to DEI—“It Takes a Village”

Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues. Key areas of focus included institutional structures, our people, our environments, and our core missions (Figure 1 and Appendix Figure 1). DHM members helped drive our work and partnered with departmental, hospital, and school of medicine committees; national organizations; and collaborators to enhance implementation and dissemination efforts. In addition to stakeholder engagement, we utilized strategic planning and rapid Plan-Do-Study-Act (PDSA) cycles to advance DEI work in our DHM.

Needs Assessment

As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.

Interventions

TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.

Our institutional structures

Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to compensation,³¹ recruitment,³² and policies that support and foster a culture of DEI.³² These strategies were used to support the following initiatives:

Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks³³ for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.

A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.

Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.³² Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.

Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.

Our People

The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.

Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.

Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.

Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.

Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. Additionally, our team members conducted environmental scans (eg, identified pictures, artwork, or other images that were not representative of a diverse and inclusive environment and raised concerns when the environment was not inclusive).

Measures

Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.

Analysis

Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.

Strategic Plan Development and Tracking

From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.

Compensation

One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.

Recruitment and Advancement

Toolkits³² and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.

Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.

We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.

Environment

We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.

Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.

We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.

DISCUSSION

The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.

We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.

By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.³⁴ By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.

Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs³⁵) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking³³ as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.

Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.

We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:

• Instituting implicit bias training for all of our faculty
• Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
• Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
• Completion of a diversity dashboard to track our progress in all of these efforts over time
• Development of a more robust pipeline to promotion and leadership for our URM faculty

This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.

Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.

Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)¹ throughout nearly all aspects of academic medicine, including rank,^2-4 tenure,⁵ authorship,^6,7 funding opportunities,^8,9 awards,¹⁰ speakership,¹¹ leadership,^12,13 and salaries.^2,14,15 Hospital medicine, despite being a newer field,¹⁶ has also seen these disparities^17,18; however, there are numerous efforts in place to actively change our specialty’s course.^19-22 Hospital medicine is a field known for being a change agent in healthcare delivery,²² and its novel approaches are well poised to fundamentally shatter the glass ceilings imposed on traditionally underrepresented groups in medicine. The importance of diversity, equity, and inclusion (DEI) initiatives in healthcare has never been clearer,^23,24 particularly as they relate to cultural competence^25-28 and cultural humility,^29,30 implicit and explicit bias,²⁷ expanding care for underserved patient populations, supporting our workforce, and broadening research agendas.²⁸

In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.

Our Division’s Framework to DEI—“It Takes a Village”

Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues. Key areas of focus included institutional structures, our people, our environments, and our core missions (Figure 1 and Appendix Figure 1). DHM members helped drive our work and partnered with departmental, hospital, and school of medicine committees; national organizations; and collaborators to enhance implementation and dissemination efforts. In addition to stakeholder engagement, we utilized strategic planning and rapid Plan-Do-Study-Act (PDSA) cycles to advance DEI work in our DHM.

Needs Assessment

As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.

Interventions

TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.

Our institutional structures

Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to compensation,³¹ recruitment,³² and policies that support and foster a culture of DEI.³² These strategies were used to support the following initiatives:

Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks³³ for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.

A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.

Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.³² Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.

Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.

Our People

The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.

Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.

Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.

Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.

Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. Additionally, our team members conducted environmental scans (eg, identified pictures, artwork, or other images that were not representative of a diverse and inclusive environment and raised concerns when the environment was not inclusive).

Measures

Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.

Analysis

Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.

Strategic Plan Development and Tracking

From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.

Compensation

One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.

Recruitment and Advancement

Toolkits³² and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.

Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.

We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.

Environment

We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.

Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.

We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.

DISCUSSION

The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.

We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.

By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.³⁴ By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.

Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs³⁵) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking³³ as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.

Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.

We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:

• Instituting implicit bias training for all of our faculty
• Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
• Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
• Completion of a diversity dashboard to track our progress in all of these efforts over time
• Development of a more robust pipeline to promotion and leadership for our URM faculty

This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.

Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Healthcare in rural areas faces increasing challenges due to community hospital closures, physician shortages, and a more concentrated population of older adults with higher rates of comorbid conditions than their urban counterparts.^1-3 Critical access hospitals (CAHs), which primarily serve rural areas, have fewer clinical capabilities, worse process-of-care measures, and higher mortality rates for some conditions when compared to non-CAHs.⁴ As such, CAHs are closing at record numbers across the United States,⁵ resulting in loss of available hospital beds and patient access to timely emergency services,⁶ which can worsen outcomes, further widening the rural-urban healthcare gap.^7,8 Furthermore, this strain on an overwhelmed health system in the most vulnerable areas restricts the ability to respond to healthcare crises like the coronavirus disease 2019 pandemic.⁹

Providing adequate staff for currently available hospital beds is also a problem in rural areas. Studies demonstrating improved outcomes, decreased length of stay (LOS), and increased quality with hospitalist services have resulted in a high demand for hospitalists nationwide.^10-12 Recruiting hospitalists to work in rural areas, however, has become increasingly challenging due to low-patient volumes, financial viability of hospitalist-model adoption, and provider shortages.^13,14 Recently, the Veterans Health Administration (VHA) reported a 28% nationwide shortage of hospitalists,¹⁵ which disproportionally affects rural VHA hospitals. Staffing difficulties and reliance on intermittent providers were reported by more than 80% of rural and low-complexity VHA facilities.¹⁶

Telehospitalist services (THS) can help deliver high-quality care to rural residents locally, decrease travel expenses, support hospital volume, and increase healthcare capacity in response to a pandemic.^14,17,18 Only a few studies have described THS (mostly with overnight or cross-coverage models directed to CAHs), and clinical outcomes have been inconsistently reported.^17,19-21 Furthermore, no program has been conducted within an integrated health system akin to the VHA. The primary objective of this quality improvement (QI) initiative was to perform a mixed-methods evaluation of THS between VHA hospitals to compare clinical outcomes and patient and staff satisfaction. Secondary outcomes included description of the implementation process, unexpected challenges, and subsequent QI initiatives. These results will expand the knowledge on feasibility of THS and provide implementation guidance.

A mixed-methods approach was used to evaluate outcomes of this QI project. Reporting follows the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0).²²

Context

The VHA is the largest integrated healthcare system in the United States, with more than 8 million veterans enrolled, more than 30% of whom reside in a rural area. The VHA comprises more than 1,000 outpatient clinics and 170 acute care VA Medical Centers,^23,24 including more than 35 rural and low-complexity hospitals.²⁵ Low-complexity hospitals are those with the lowest volume and levels of patient complexity and minimal or no teaching programs, research, intensive care unit (ICU) beds, and subspecialists. Lack of reimbursement and interstate licensing, often cited as barriers to telemedicine, do not apply to the VHA. The hub site was a large tertiary care (high-complexity) VHA hospital located in Iowa City, Iowa. The spoke site was a low-complexity (10-bed acute inpatient unit with no ICU) rural VA hospital located in Tomah, Wisconsin.

Study Population

The preimplementation cohort for comparison included all patients admitted between January 1, 2018, and January 6, 2019. The postimplementation study cohort included all observation and acute care admissions during the pilot phase (January 7 to May 3, 2019) and sustainability phase (July 15 to December 31, 2019). The postimplementation analysis excluded the time period of May 4 to July 14, 2019, due to an interruption (gap) in THS. The gap period allowed for preliminary data analysis, optimization of the telecommunication system, and the recruitment and training of additional providers who could provide long-term staffing to the service.

Intervention

Preimplementation

Prior to THS implementation, Tomah’s inpatient ward was staffed by one physician per shift, who could be a hospitalist, medical officer of the day (MOD), or an intermittent provider (locum tenens). Hospitalists covering the acute inpatient ward prior to the THS transitioned to cover weekends, nights, and urgent care service shifts.

We visited the spoke site and held information-sharing sessions with key stakeholders (administrators, clinician leaders, nurses, and ancillary staff) prior to kick-off. Recurrent phone meetings addressed anticipated and emerging challenges. Telehospitalist and local providers underwent technology and service training.

Technology and Connectivity

A low-cost technology system using tablet computers provided Health Insurance Portability and Accountability Act–compliant videoconferencing with a telehospitalist at the hub site. An Eko-Core digital stethoscope^® with a web-based audio stream was available. Telehospitalists conducted encounters from a private office space with telehealth capabilities. A total of $9,000 was spent on equipment at both sites. Due to connectivity problems and data limits, the tablets were switched to mobile computer-on-wheels workstations and hospital-based Wi-Fi for the sustainability phase.

THS Description

An experienced hub hospitalist, together with an advanced practice provider (APP; nurse practitioner [NP] or physician assistant [PA]), cared for all patients admitted to the 10-bed inpatient unit at the spoke site, Monday through Friday from 8:00 AM to 4:30 PM. The APP had limited or no prior experience in acute inpatient medicine. The telehospitalist worked as a team with the APP. The APP was the main point of contact for nurses, performed physical examinations, and directed patient care to their level of comfort (in a similar manner as a teaching team). The telehospitalist conducted bedside patient rounds, participated in multidisciplinary huddles, and shared clinical documentation and administrative duties with the APP. The telehospitalist was the primary staff for admitted patients and had full access to the electronic health record (EHR). The THS was staffed by 10 hospitalists during the study period. Overnight and weekend cross-coverage and admissions were performed by MODs, who also covered the urgent care and cross-covered other nonmedical units.

Quantitative Evaluation Methods

Workload and Clinical Outcomes

An EHR query identified all patients admitted during the pre- and postimplementation periods. Demographic data, clinical Nosos risk scores,^26,27 and top admission diagnoses were reported. Workload was evaluated using the average number of encounters per day and self-reported telehospitalist worksheets, which were cross-referenced with EHR data. Clinical outcomes included LOS, 30-day hospital readmission rate, 30-day standardized mortality (SMR30), in-hospital mortality, and VHA-specific inpatient quality metrics. Independent sample t tests for continuous variables and chi-square tests or Fisher’s exact test (for patient class) for categorical variables were used to compare pre- and postimplementation groups. Statistical process control (SPC) charts evaluated changes over time. All analyses were conducted using Microsoft Excel and R.²⁸

Provider Satisfaction

Anonymous surveys were distributed to spoke-site inpatient and administrative staff at 1 month and 12 months postimplementation, assessing satisfaction, technology/connectivity, communication, and challenges (Appendix Figure 1). Satisfaction of the telehospitalist physicians at the hub site was measured 12 months postimplementation by a 26-question survey assessing the same domains, plus quality of care (Appendix Figure 2).

Patient Satisfaction

The VHA Survey of Healthcare Experiences of Patients (SHEP), a version of the Hospital Consumer Assessment of Healthcare Providers and Systems Survey,^29,30 was mailed to all patients after discharge. Survey responses concerning inpatient provider care (eg, care coordination, communication, hospital rating, willingness to recommend the hospital) during the pre- and postimplementation phases were compared using a two-sample test of independent proportions. Responses obtained during May and June 2019 were excluded.

Qualitative Evaluation Methods

The qualitative researcher observed information-sharing meetings and facilitated unstructured interviews with clinical and administrative staff during site visits preimplementation and 3 months after implementation. Interviews with administrators and clinical staff addressed their experiences with the THS, staff’s perception of patient and family response to THS implementation, administrative impacts, challenges, and strengths. All interviews and meetings were documented with handwritten notes and audio recordings. Interview summary notes were typed into a Microsoft Word document, verified by the physician-investigator, and synthesized by inductive themes into site-visit reports. Audio recordings were uploaded to a secure computer, transcribed, and reviewed for accuracy. The qualitative researcher also identified emerging themes from open-ended survey responses. Process evaluation findings were shared with administration at the spoke site.

The authors had full access to, and took full responsibility for, the integrity of the data. The project was evaluated by the University of Iowa Institutional Review Board and the Iowa City VA Research and Development Committee and was determined to be a non–human-subjects QI project.

Quantitative Workload and Clinical Outcomes

There were 822 admissions during the preimplementation period and 550 admissions during the postimplementation period (253 during the pilot and 297 during sustainability phase). Patient characteristics pre- and postimplementation were not significantly different (Table 1). The median patient age was 65 years; 96% of patients were male, and 83% were rural residents. The most common admission diagnosis was alcohol-related (36%); regarding patient disposition, 78% of admissions were discharged home.

Workload

There were 502 patient encounters staffed by the telehospitalist in the pilot phase, with an average of 6.25 encounters per day, and a telehospitalist-reported workload of 7 hours per day. There were 538 patient encounters, with an average of 4.67 encounters per day and a workload of 5.6 hours per day in the sustainability phase. The average daily census decreased from 5.0 (SD, 1.1) patients per day during preimplementation to 3.1 (SD, 0.5) patients per day during postimplementation (Table 2). In some of the months during the study period, admissions decreased below the lower SPC limit, suggesting a significant change (Figure). Adjusted LOS was significantly lower, with 3.0 (SD, 0.7) days vs 2.3 (SD, 0.3) days in the pre- and postimplementation periods, respectively. Bed occupancy rates were significantly lower in the sustainability phase compared with the pilot phase and the preimplementation period. Readmission rates varied, ranging from <10% to >30%, not significantly different but slightly higher in the postimplementation period. Readmission rates for heart failure, chronic obstructive pulmonary disease, and pneumonia remained unchanged; other medical readmissions (mostly alcohol-related) were slightly higher in the postimplementation period.

In-hospital mortality and SMR30 did not change significantly, but there was improvement in the 12-month rolling average of the observed/expected SMR30 from 1.40 to 1.08. Additional VHA-specific quality metrics were monitored and showed either small improvements or no change (data not shown).

Satisfaction at Hub and Spoke Sites

After sending two reminder communications via email, the telehospitalist satisfaction survey had a total response rate of 90% (9/10). Telehospitalists were satisfied or very satisfied (89%) with the program and the local providers (88.9%), rating their experience as good or excellent (100%) (Table 3). Communication with patients, families, and local staff was noted as being “positive” or “mostly positive.” Telehospitalists reported confidence in the accuracy of their diagnoses and rated the quality of care as being equal to that of a face-to-face encounter. Connectivity problems were prevalent, although most providers were able to resort to a back-up plan. Other challenges included differences in culture and concerns about liability. We received 27 responses from the spoke-site satisfaction survey; the response rate could not be determined because the survey was distributed by the spoke site for anonymity. Of the respondents, 37% identified as nurses, 25.9% as healthcare providers (APPs or physicians), and 33.3% as other staff (eg, social worker, nutritionist, physical therapist, utilization management, administrators); 3.7% did not respond. Among the participants, 88% had personally interacted with the THS. Most providers and other staff perceived THS as valuable (57.1% and 77.8%, respectively) and were satisfied or highly satisfied with THS (57.1% and 55.6%, respectively). On average, nurses provided lower ratings across all survey items than providers and other staff. Challenges noted by all staff included issues with communication, workflow, and technology/connectivity.

Regarding patient satisfaction, the SHEP survey showed a significant improvement in care coordination (18%; P = .02) and a nonsignificant improvement in communications about medications (5%; P =.054). The remaining items in the survey, including overall hospital rating and willingness to recommend the hospital, were unchanged (Appendix Table).

Qualitative Strengths

Our process evaluation identified high quality of care and teamwork as contributors to the success of the program. Overall, staff credited perceived improvements in quality of care to the quality of providers staffing the THS, including the local APPs. Noting the telehospitalists’ knowledge base and level of engagement as key attributes, one staff member commented: “I prefer a telehospitalist that really care[s] about patients than some provider that is physically here but does not engage.” Staff perceived improvements in the continuity of care, as well as care processes such as handoffs and transitions of care.

Improvements in teamwork were perceived compared with the previous model of care. Telehospitalists were lauded for their professionalism and communication skills. Overall, nurses felt providers in the THS listened more to their views. In addition, nurse respondents felt they could learn from several providers and said they enjoyed the telehospitalists’ disposition to teach and discuss patient care. The responsiveness of the THS staff was instrumental in building teamwork and acceptance. A bedside interdisciplinary protocol was established for appropriate patients. Local staff felt this was crucial for teamwork and patient satisfaction. Telehospitalists reported high-value in interdisciplinary rounds, facilitating interaction with nurses and ancillary staff. Handoff problems were identified, leading to QI initiatives to mitigate those issues.

Challenges

The survey identified administrative barriers, technical difficulties, workflow constraints, and clinical concerns. The credentialing process was complicated, delaying the onboarding of telehospitalists. Internet connectivity was inconsistent, leading to disruption in video communications; however, during the sustainability phase, updated technology improved communications. The communication workflow was resisted by some nurses, who wanted to phone the telehospitalist directly rather than having the local APP as the first contact. Secure messaging was enabled to allow nurses direct contact during the sustainability phase.

Workload was a concern among telehospitalists and local staff. Telehospitalists perceived the documentation requirements and administrative workload to be two to three times higher than at other hospitals—despite the lower number of encounters. Finally, clinical concerns from spoke-site clinicians included a perceived rise in the acuity of patients (which was not evident by the Nosos score) and delayed decisions to transfer-out patients. These concerns were addressed with educational sessions for telehospitalists during the sustainability phase.

Additional Quality Improvement Projects

The implementation of THS resulted in QI initiatives at the spoke site, including an EHR-integrated handoff tool; a documentation evaluation that led to the elimination of duplicative, inefficient, and error-prone templates; and a revision of the alcohol withdrawal treatment protocol during the sustainability phase to reduce the use of intravenous benzodiazepines. A more comprehensive benzodiazepine-sparing alcohol withdrawal treatment protocol was also developed but was not implemented until after the study period (January 2020).

Our pre-post study evaluation found implementation of a THS to be noninferior to face-to-face care, with no significant change in mortality, readmission rate, or patient satisfaction. The significant improvement observed in LOS is consistent with the adoption of hospitalist models in other medical care settings,¹¹ but had not been reported by previous telehospitalist studies. For example, in their retrospective chart review comparing an NP-supported telehospitalist model to locum tenens hospitalists, Boltz et al found no difference in LOS.³¹ Moreover, as in our study, they found no differences in readmissions, mortality, and patient satisfaction.³¹ Similarly, Kuperman et al reported unchanged daily census, LOS, and transfer rates from a CAH with their virtual hospitalist program, but a decrease in the percentage of patients transferred-out from the emergency department, suggesting that more patients were treated locally.¹⁹

Reduction in LOS is one of the primary measures of efficiency in hospital care³¹; reducing LOS while maintaining the quality of care lowers hospital costs. The reduction in LOS in our study could be attributed to greater continuity of care, engagement/experience of the telehospitalists, or other factors. This decrease in LOS and slight reduction in admissions resulted in an overall lower daily census during the study period and impacted efficiency. Our study was unable to determine the cause for the reduction in admissions; however, several concurrent events, including the expansion of community-care options for veterans under the MISSION ACT (Maintaining Internal Systems and Strengthening Integrated Outside Networks Act) in June 2019, a nationwide smoking ban at VA facilities (October 2019), and a modification in the alcohol withdrawal treatment protocol might have influenced veterans’ choice of hospital.

Readmission rates were slightly higher, though nonsignificant, in the postimplementation period. Alcohol-related readmissions accounted for most readmissions; some of the protocol changes, such as admitting all patients with alcohol withdrawal to inpatient class instead of admitting some to the observation class, accounted for part of the increase in readmission rates. Readmission rates for other conditions such as chronic obstructive pulmonary disease, chronic heart failure, or pneumonia were not significantly different, suggesting that the reduction in LOS did not result in an unintended increased readmission rate for those conditions.

Rural hospitals are struggling with staffing and finances. Resorting to locum tenens staffing is costly and can result in variable quality of care.^32,33 APPs are increasingly taking on hospitalist positions, with 65% of adult hospitalist programs, including half of all VHA hospitals, employing NPs and PAs.^34,35 In response to this expanded scope of practice, hospitals employing APPs in hospitalist roles must comply with state and federal laws, which often require that APPs be supervised by or work in collaboration with an on-site or off-site physician. The THS is a great model to support APPs and address staffing and cost challenges in low-volume rural facilities, while maintaining quality of care. Some APP-telehospitalist programs similar to ours have reported cost reductions of up 58% compared to programs that employ locum tenens physicians.³¹ In our model, we assume that a single telehospitalist hub could provide coverage to two or three spoke sites with APP support, reducing staffing costs.

Hub telehospitalists reported satisfaction with the program, and they perceived the quality of care to be comparable to face-to-face encounters; their responses were consistent with those previously reported in an evaluation of telemedicine acute care by JaKa et al.²⁰ Spoke-site staff, however, had a mixed level of satisfaction, which was different from responses reported by JaKa et al.²⁰ The primary challenges encountered were technological and communication issues, differences in cultures of care between the hub and the spoke sites, and buy-in from frontline staff. Differences in expectations and unclear role definitions between the local APP and the telehospitalist were identified as contributors to dissatisfaction with the program by the nursing staff. Modifications to the communication processes between nurses and telehospitalists and role clarification improved the experience. Culture and practice differences between spoke physicians and the telehospitalist persisted throughout the program implementation, and likely affected the hub providers’ perception of the THS. This was evidenced by reluctance from spoke physicians to implement warm handoffs or participate in THS meetings and resistance to protocol changes. Additional evaluations, collaborations. and interventions are required to improve satisfaction of spoke-site staff.

This study has several limitations. First, the VHA is an integrated health system, one that serves an older, predominantly male patient population. Also, the lack of reimbursement and interstate licensing restrictions limit generalizability of these results to other CAHs or healthcare systems. Furthermore, the intervention was limited to a single rural site; while this allowed for a detailed evaluation, unique barriers or facilitators might exist that limit its applicability. In addition, QI initiatives implemented by the VHA during the project period might have confounded some of our results. Last, patient satisfaction survey data are overall limited in their ability to fully assess patient’s experience and satisfaction with the program. Further qualitative studies are needed to gain deeper insight into patient perspectives with the THS and whether modality of care delivery influences patients’ care decisions. Future studies should consider a multisite design with one or more hubs and multiple spoke sites.

Telehospitalist services are a feasible and safe approach to provide inpatient services and address staffing needs of rural hospitals. To enhance program performance, it is essential to ensure adequate technological quality, clearly delineate and define roles and responsibilities of the care team, and address communication issues or staff concerns in a timely manner.

The authors thank the staff, administration, and leadership at the Tomah and Iowa City VA Medical Centers for working with us on this project. They offer special thanks to Kevin Glenn, MD, MS, Ethan Kuperman, MD, MS, FHM, and Jennifer Chapin, MSN, RN, for sharing their expertise, and the telehealth team, including Nathaniel Samuelson, Angela McDowell, and Katrin Metcalf.

The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Healthcare in rural areas faces increasing challenges due to community hospital closures, physician shortages, and a more concentrated population of older adults with higher rates of comorbid conditions than their urban counterparts.^1-3 Critical access hospitals (CAHs), which primarily serve rural areas, have fewer clinical capabilities, worse process-of-care measures, and higher mortality rates for some conditions when compared to non-CAHs.⁴ As such, CAHs are closing at record numbers across the United States,⁵ resulting in loss of available hospital beds and patient access to timely emergency services,⁶ which can worsen outcomes, further widening the rural-urban healthcare gap.^7,8 Furthermore, this strain on an overwhelmed health system in the most vulnerable areas restricts the ability to respond to healthcare crises like the coronavirus disease 2019 pandemic.⁹

Providing adequate staff for currently available hospital beds is also a problem in rural areas. Studies demonstrating improved outcomes, decreased length of stay (LOS), and increased quality with hospitalist services have resulted in a high demand for hospitalists nationwide.^10-12 Recruiting hospitalists to work in rural areas, however, has become increasingly challenging due to low-patient volumes, financial viability of hospitalist-model adoption, and provider shortages.^13,14 Recently, the Veterans Health Administration (VHA) reported a 28% nationwide shortage of hospitalists,¹⁵ which disproportionally affects rural VHA hospitals. Staffing difficulties and reliance on intermittent providers were reported by more than 80% of rural and low-complexity VHA facilities.¹⁶

Telehospitalist services (THS) can help deliver high-quality care to rural residents locally, decrease travel expenses, support hospital volume, and increase healthcare capacity in response to a pandemic.^14,17,18 Only a few studies have described THS (mostly with overnight or cross-coverage models directed to CAHs), and clinical outcomes have been inconsistently reported.^17,19-21 Furthermore, no program has been conducted within an integrated health system akin to the VHA. The primary objective of this quality improvement (QI) initiative was to perform a mixed-methods evaluation of THS between VHA hospitals to compare clinical outcomes and patient and staff satisfaction. Secondary outcomes included description of the implementation process, unexpected challenges, and subsequent QI initiatives. These results will expand the knowledge on feasibility of THS and provide implementation guidance.

A mixed-methods approach was used to evaluate outcomes of this QI project. Reporting follows the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0).²²

Context

The VHA is the largest integrated healthcare system in the United States, with more than 8 million veterans enrolled, more than 30% of whom reside in a rural area. The VHA comprises more than 1,000 outpatient clinics and 170 acute care VA Medical Centers,^23,24 including more than 35 rural and low-complexity hospitals.²⁵ Low-complexity hospitals are those with the lowest volume and levels of patient complexity and minimal or no teaching programs, research, intensive care unit (ICU) beds, and subspecialists. Lack of reimbursement and interstate licensing, often cited as barriers to telemedicine, do not apply to the VHA. The hub site was a large tertiary care (high-complexity) VHA hospital located in Iowa City, Iowa. The spoke site was a low-complexity (10-bed acute inpatient unit with no ICU) rural VA hospital located in Tomah, Wisconsin.

Study Population

The preimplementation cohort for comparison included all patients admitted between January 1, 2018, and January 6, 2019. The postimplementation study cohort included all observation and acute care admissions during the pilot phase (January 7 to May 3, 2019) and sustainability phase (July 15 to December 31, 2019). The postimplementation analysis excluded the time period of May 4 to July 14, 2019, due to an interruption (gap) in THS. The gap period allowed for preliminary data analysis, optimization of the telecommunication system, and the recruitment and training of additional providers who could provide long-term staffing to the service.

Intervention

Preimplementation

Prior to THS implementation, Tomah’s inpatient ward was staffed by one physician per shift, who could be a hospitalist, medical officer of the day (MOD), or an intermittent provider (locum tenens). Hospitalists covering the acute inpatient ward prior to the THS transitioned to cover weekends, nights, and urgent care service shifts.

We visited the spoke site and held information-sharing sessions with key stakeholders (administrators, clinician leaders, nurses, and ancillary staff) prior to kick-off. Recurrent phone meetings addressed anticipated and emerging challenges. Telehospitalist and local providers underwent technology and service training.

Technology and Connectivity

A low-cost technology system using tablet computers provided Health Insurance Portability and Accountability Act–compliant videoconferencing with a telehospitalist at the hub site. An Eko-Core digital stethoscope^® with a web-based audio stream was available. Telehospitalists conducted encounters from a private office space with telehealth capabilities. A total of $9,000 was spent on equipment at both sites. Due to connectivity problems and data limits, the tablets were switched to mobile computer-on-wheels workstations and hospital-based Wi-Fi for the sustainability phase.

THS Description

An experienced hub hospitalist, together with an advanced practice provider (APP; nurse practitioner [NP] or physician assistant [PA]), cared for all patients admitted to the 10-bed inpatient unit at the spoke site, Monday through Friday from 8:00 AM to 4:30 PM. The APP had limited or no prior experience in acute inpatient medicine. The telehospitalist worked as a team with the APP. The APP was the main point of contact for nurses, performed physical examinations, and directed patient care to their level of comfort (in a similar manner as a teaching team). The telehospitalist conducted bedside patient rounds, participated in multidisciplinary huddles, and shared clinical documentation and administrative duties with the APP. The telehospitalist was the primary staff for admitted patients and had full access to the electronic health record (EHR). The THS was staffed by 10 hospitalists during the study period. Overnight and weekend cross-coverage and admissions were performed by MODs, who also covered the urgent care and cross-covered other nonmedical units.

Quantitative Evaluation Methods

Workload and Clinical Outcomes

An EHR query identified all patients admitted during the pre- and postimplementation periods. Demographic data, clinical Nosos risk scores,^26,27 and top admission diagnoses were reported. Workload was evaluated using the average number of encounters per day and self-reported telehospitalist worksheets, which were cross-referenced with EHR data. Clinical outcomes included LOS, 30-day hospital readmission rate, 30-day standardized mortality (SMR30), in-hospital mortality, and VHA-specific inpatient quality metrics. Independent sample t tests for continuous variables and chi-square tests or Fisher’s exact test (for patient class) for categorical variables were used to compare pre- and postimplementation groups. Statistical process control (SPC) charts evaluated changes over time. All analyses were conducted using Microsoft Excel and R.²⁸

Provider Satisfaction

Anonymous surveys were distributed to spoke-site inpatient and administrative staff at 1 month and 12 months postimplementation, assessing satisfaction, technology/connectivity, communication, and challenges (Appendix Figure 1). Satisfaction of the telehospitalist physicians at the hub site was measured 12 months postimplementation by a 26-question survey assessing the same domains, plus quality of care (Appendix Figure 2).

Patient Satisfaction

The VHA Survey of Healthcare Experiences of Patients (SHEP), a version of the Hospital Consumer Assessment of Healthcare Providers and Systems Survey,^29,30 was mailed to all patients after discharge. Survey responses concerning inpatient provider care (eg, care coordination, communication, hospital rating, willingness to recommend the hospital) during the pre- and postimplementation phases were compared using a two-sample test of independent proportions. Responses obtained during May and June 2019 were excluded.

Qualitative Evaluation Methods

The qualitative researcher observed information-sharing meetings and facilitated unstructured interviews with clinical and administrative staff during site visits preimplementation and 3 months after implementation. Interviews with administrators and clinical staff addressed their experiences with the THS, staff’s perception of patient and family response to THS implementation, administrative impacts, challenges, and strengths. All interviews and meetings were documented with handwritten notes and audio recordings. Interview summary notes were typed into a Microsoft Word document, verified by the physician-investigator, and synthesized by inductive themes into site-visit reports. Audio recordings were uploaded to a secure computer, transcribed, and reviewed for accuracy. The qualitative researcher also identified emerging themes from open-ended survey responses. Process evaluation findings were shared with administration at the spoke site.

The authors had full access to, and took full responsibility for, the integrity of the data. The project was evaluated by the University of Iowa Institutional Review Board and the Iowa City VA Research and Development Committee and was determined to be a non–human-subjects QI project.

Quantitative Workload and Clinical Outcomes

There were 822 admissions during the preimplementation period and 550 admissions during the postimplementation period (253 during the pilot and 297 during sustainability phase). Patient characteristics pre- and postimplementation were not significantly different (Table 1). The median patient age was 65 years; 96% of patients were male, and 83% were rural residents. The most common admission diagnosis was alcohol-related (36%); regarding patient disposition, 78% of admissions were discharged home.

Workload

There were 502 patient encounters staffed by the telehospitalist in the pilot phase, with an average of 6.25 encounters per day, and a telehospitalist-reported workload of 7 hours per day. There were 538 patient encounters, with an average of 4.67 encounters per day and a workload of 5.6 hours per day in the sustainability phase. The average daily census decreased from 5.0 (SD, 1.1) patients per day during preimplementation to 3.1 (SD, 0.5) patients per day during postimplementation (Table 2). In some of the months during the study period, admissions decreased below the lower SPC limit, suggesting a significant change (Figure). Adjusted LOS was significantly lower, with 3.0 (SD, 0.7) days vs 2.3 (SD, 0.3) days in the pre- and postimplementation periods, respectively. Bed occupancy rates were significantly lower in the sustainability phase compared with the pilot phase and the preimplementation period. Readmission rates varied, ranging from <10% to >30%, not significantly different but slightly higher in the postimplementation period. Readmission rates for heart failure, chronic obstructive pulmonary disease, and pneumonia remained unchanged; other medical readmissions (mostly alcohol-related) were slightly higher in the postimplementation period.

In-hospital mortality and SMR30 did not change significantly, but there was improvement in the 12-month rolling average of the observed/expected SMR30 from 1.40 to 1.08. Additional VHA-specific quality metrics were monitored and showed either small improvements or no change (data not shown).

Satisfaction at Hub and Spoke Sites

After sending two reminder communications via email, the telehospitalist satisfaction survey had a total response rate of 90% (9/10). Telehospitalists were satisfied or very satisfied (89%) with the program and the local providers (88.9%), rating their experience as good or excellent (100%) (Table 3). Communication with patients, families, and local staff was noted as being “positive” or “mostly positive.” Telehospitalists reported confidence in the accuracy of their diagnoses and rated the quality of care as being equal to that of a face-to-face encounter. Connectivity problems were prevalent, although most providers were able to resort to a back-up plan. Other challenges included differences in culture and concerns about liability. We received 27 responses from the spoke-site satisfaction survey; the response rate could not be determined because the survey was distributed by the spoke site for anonymity. Of the respondents, 37% identified as nurses, 25.9% as healthcare providers (APPs or physicians), and 33.3% as other staff (eg, social worker, nutritionist, physical therapist, utilization management, administrators); 3.7% did not respond. Among the participants, 88% had personally interacted with the THS. Most providers and other staff perceived THS as valuable (57.1% and 77.8%, respectively) and were satisfied or highly satisfied with THS (57.1% and 55.6%, respectively). On average, nurses provided lower ratings across all survey items than providers and other staff. Challenges noted by all staff included issues with communication, workflow, and technology/connectivity.

Regarding patient satisfaction, the SHEP survey showed a significant improvement in care coordination (18%; P = .02) and a nonsignificant improvement in communications about medications (5%; P =.054). The remaining items in the survey, including overall hospital rating and willingness to recommend the hospital, were unchanged (Appendix Table).

Qualitative Strengths

Our process evaluation identified high quality of care and teamwork as contributors to the success of the program. Overall, staff credited perceived improvements in quality of care to the quality of providers staffing the THS, including the local APPs. Noting the telehospitalists’ knowledge base and level of engagement as key attributes, one staff member commented: “I prefer a telehospitalist that really care[s] about patients than some provider that is physically here but does not engage.” Staff perceived improvements in the continuity of care, as well as care processes such as handoffs and transitions of care.

Improvements in teamwork were perceived compared with the previous model of care. Telehospitalists were lauded for their professionalism and communication skills. Overall, nurses felt providers in the THS listened more to their views. In addition, nurse respondents felt they could learn from several providers and said they enjoyed the telehospitalists’ disposition to teach and discuss patient care. The responsiveness of the THS staff was instrumental in building teamwork and acceptance. A bedside interdisciplinary protocol was established for appropriate patients. Local staff felt this was crucial for teamwork and patient satisfaction. Telehospitalists reported high-value in interdisciplinary rounds, facilitating interaction with nurses and ancillary staff. Handoff problems were identified, leading to QI initiatives to mitigate those issues.

Challenges

The survey identified administrative barriers, technical difficulties, workflow constraints, and clinical concerns. The credentialing process was complicated, delaying the onboarding of telehospitalists. Internet connectivity was inconsistent, leading to disruption in video communications; however, during the sustainability phase, updated technology improved communications. The communication workflow was resisted by some nurses, who wanted to phone the telehospitalist directly rather than having the local APP as the first contact. Secure messaging was enabled to allow nurses direct contact during the sustainability phase.

Workload was a concern among telehospitalists and local staff. Telehospitalists perceived the documentation requirements and administrative workload to be two to three times higher than at other hospitals—despite the lower number of encounters. Finally, clinical concerns from spoke-site clinicians included a perceived rise in the acuity of patients (which was not evident by the Nosos score) and delayed decisions to transfer-out patients. These concerns were addressed with educational sessions for telehospitalists during the sustainability phase.

Additional Quality Improvement Projects

The implementation of THS resulted in QI initiatives at the spoke site, including an EHR-integrated handoff tool; a documentation evaluation that led to the elimination of duplicative, inefficient, and error-prone templates; and a revision of the alcohol withdrawal treatment protocol during the sustainability phase to reduce the use of intravenous benzodiazepines. A more comprehensive benzodiazepine-sparing alcohol withdrawal treatment protocol was also developed but was not implemented until after the study period (January 2020).

Our pre-post study evaluation found implementation of a THS to be noninferior to face-to-face care, with no significant change in mortality, readmission rate, or patient satisfaction. The significant improvement observed in LOS is consistent with the adoption of hospitalist models in other medical care settings,¹¹ but had not been reported by previous telehospitalist studies. For example, in their retrospective chart review comparing an NP-supported telehospitalist model to locum tenens hospitalists, Boltz et al found no difference in LOS.³¹ Moreover, as in our study, they found no differences in readmissions, mortality, and patient satisfaction.³¹ Similarly, Kuperman et al reported unchanged daily census, LOS, and transfer rates from a CAH with their virtual hospitalist program, but a decrease in the percentage of patients transferred-out from the emergency department, suggesting that more patients were treated locally.¹⁹

Reduction in LOS is one of the primary measures of efficiency in hospital care³¹; reducing LOS while maintaining the quality of care lowers hospital costs. The reduction in LOS in our study could be attributed to greater continuity of care, engagement/experience of the telehospitalists, or other factors. This decrease in LOS and slight reduction in admissions resulted in an overall lower daily census during the study period and impacted efficiency. Our study was unable to determine the cause for the reduction in admissions; however, several concurrent events, including the expansion of community-care options for veterans under the MISSION ACT (Maintaining Internal Systems and Strengthening Integrated Outside Networks Act) in June 2019, a nationwide smoking ban at VA facilities (October 2019), and a modification in the alcohol withdrawal treatment protocol might have influenced veterans’ choice of hospital.

Readmission rates were slightly higher, though nonsignificant, in the postimplementation period. Alcohol-related readmissions accounted for most readmissions; some of the protocol changes, such as admitting all patients with alcohol withdrawal to inpatient class instead of admitting some to the observation class, accounted for part of the increase in readmission rates. Readmission rates for other conditions such as chronic obstructive pulmonary disease, chronic heart failure, or pneumonia were not significantly different, suggesting that the reduction in LOS did not result in an unintended increased readmission rate for those conditions.

Rural hospitals are struggling with staffing and finances. Resorting to locum tenens staffing is costly and can result in variable quality of care.^32,33 APPs are increasingly taking on hospitalist positions, with 65% of adult hospitalist programs, including half of all VHA hospitals, employing NPs and PAs.^34,35 In response to this expanded scope of practice, hospitals employing APPs in hospitalist roles must comply with state and federal laws, which often require that APPs be supervised by or work in collaboration with an on-site or off-site physician. The THS is a great model to support APPs and address staffing and cost challenges in low-volume rural facilities, while maintaining quality of care. Some APP-telehospitalist programs similar to ours have reported cost reductions of up 58% compared to programs that employ locum tenens physicians.³¹ In our model, we assume that a single telehospitalist hub could provide coverage to two or three spoke sites with APP support, reducing staffing costs.

Hub telehospitalists reported satisfaction with the program, and they perceived the quality of care to be comparable to face-to-face encounters; their responses were consistent with those previously reported in an evaluation of telemedicine acute care by JaKa et al.²⁰ Spoke-site staff, however, had a mixed level of satisfaction, which was different from responses reported by JaKa et al.²⁰ The primary challenges encountered were technological and communication issues, differences in cultures of care between the hub and the spoke sites, and buy-in from frontline staff. Differences in expectations and unclear role definitions between the local APP and the telehospitalist were identified as contributors to dissatisfaction with the program by the nursing staff. Modifications to the communication processes between nurses and telehospitalists and role clarification improved the experience. Culture and practice differences between spoke physicians and the telehospitalist persisted throughout the program implementation, and likely affected the hub providers’ perception of the THS. This was evidenced by reluctance from spoke physicians to implement warm handoffs or participate in THS meetings and resistance to protocol changes. Additional evaluations, collaborations. and interventions are required to improve satisfaction of spoke-site staff.

This study has several limitations. First, the VHA is an integrated health system, one that serves an older, predominantly male patient population. Also, the lack of reimbursement and interstate licensing restrictions limit generalizability of these results to other CAHs or healthcare systems. Furthermore, the intervention was limited to a single rural site; while this allowed for a detailed evaluation, unique barriers or facilitators might exist that limit its applicability. In addition, QI initiatives implemented by the VHA during the project period might have confounded some of our results. Last, patient satisfaction survey data are overall limited in their ability to fully assess patient’s experience and satisfaction with the program. Further qualitative studies are needed to gain deeper insight into patient perspectives with the THS and whether modality of care delivery influences patients’ care decisions. Future studies should consider a multisite design with one or more hubs and multiple spoke sites.

Telehospitalist services are a feasible and safe approach to provide inpatient services and address staffing needs of rural hospitals. To enhance program performance, it is essential to ensure adequate technological quality, clearly delineate and define roles and responsibilities of the care team, and address communication issues or staff concerns in a timely manner.

The authors thank the staff, administration, and leadership at the Tomah and Iowa City VA Medical Centers for working with us on this project. They offer special thanks to Kevin Glenn, MD, MS, Ethan Kuperman, MD, MS, FHM, and Jennifer Chapin, MSN, RN, for sharing their expertise, and the telehealth team, including Nathaniel Samuelson, Angela McDowell, and Katrin Metcalf.

The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Healthcare in rural areas faces increasing challenges due to community hospital closures, physician shortages, and a more concentrated population of older adults with higher rates of comorbid conditions than their urban counterparts.^1-3 Critical access hospitals (CAHs), which primarily serve rural areas, have fewer clinical capabilities, worse process-of-care measures, and higher mortality rates for some conditions when compared to non-CAHs.⁴ As such, CAHs are closing at record numbers across the United States,⁵ resulting in loss of available hospital beds and patient access to timely emergency services,⁶ which can worsen outcomes, further widening the rural-urban healthcare gap.^7,8 Furthermore, this strain on an overwhelmed health system in the most vulnerable areas restricts the ability to respond to healthcare crises like the coronavirus disease 2019 pandemic.⁹

Providing adequate staff for currently available hospital beds is also a problem in rural areas. Studies demonstrating improved outcomes, decreased length of stay (LOS), and increased quality with hospitalist services have resulted in a high demand for hospitalists nationwide.^10-12 Recruiting hospitalists to work in rural areas, however, has become increasingly challenging due to low-patient volumes, financial viability of hospitalist-model adoption, and provider shortages.^13,14 Recently, the Veterans Health Administration (VHA) reported a 28% nationwide shortage of hospitalists,¹⁵ which disproportionally affects rural VHA hospitals. Staffing difficulties and reliance on intermittent providers were reported by more than 80% of rural and low-complexity VHA facilities.¹⁶

Telehospitalist services (THS) can help deliver high-quality care to rural residents locally, decrease travel expenses, support hospital volume, and increase healthcare capacity in response to a pandemic.^14,17,18 Only a few studies have described THS (mostly with overnight or cross-coverage models directed to CAHs), and clinical outcomes have been inconsistently reported.^17,19-21 Furthermore, no program has been conducted within an integrated health system akin to the VHA. The primary objective of this quality improvement (QI) initiative was to perform a mixed-methods evaluation of THS between VHA hospitals to compare clinical outcomes and patient and staff satisfaction. Secondary outcomes included description of the implementation process, unexpected challenges, and subsequent QI initiatives. These results will expand the knowledge on feasibility of THS and provide implementation guidance.

A mixed-methods approach was used to evaluate outcomes of this QI project. Reporting follows the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0).²²

Context

The VHA is the largest integrated healthcare system in the United States, with more than 8 million veterans enrolled, more than 30% of whom reside in a rural area. The VHA comprises more than 1,000 outpatient clinics and 170 acute care VA Medical Centers,^23,24 including more than 35 rural and low-complexity hospitals.²⁵ Low-complexity hospitals are those with the lowest volume and levels of patient complexity and minimal or no teaching programs, research, intensive care unit (ICU) beds, and subspecialists. Lack of reimbursement and interstate licensing, often cited as barriers to telemedicine, do not apply to the VHA. The hub site was a large tertiary care (high-complexity) VHA hospital located in Iowa City, Iowa. The spoke site was a low-complexity (10-bed acute inpatient unit with no ICU) rural VA hospital located in Tomah, Wisconsin.

Study Population

The preimplementation cohort for comparison included all patients admitted between January 1, 2018, and January 6, 2019. The postimplementation study cohort included all observation and acute care admissions during the pilot phase (January 7 to May 3, 2019) and sustainability phase (July 15 to December 31, 2019). The postimplementation analysis excluded the time period of May 4 to July 14, 2019, due to an interruption (gap) in THS. The gap period allowed for preliminary data analysis, optimization of the telecommunication system, and the recruitment and training of additional providers who could provide long-term staffing to the service.

Intervention

Preimplementation

Prior to THS implementation, Tomah’s inpatient ward was staffed by one physician per shift, who could be a hospitalist, medical officer of the day (MOD), or an intermittent provider (locum tenens). Hospitalists covering the acute inpatient ward prior to the THS transitioned to cover weekends, nights, and urgent care service shifts.

We visited the spoke site and held information-sharing sessions with key stakeholders (administrators, clinician leaders, nurses, and ancillary staff) prior to kick-off. Recurrent phone meetings addressed anticipated and emerging challenges. Telehospitalist and local providers underwent technology and service training.

Technology and Connectivity

A low-cost technology system using tablet computers provided Health Insurance Portability and Accountability Act–compliant videoconferencing with a telehospitalist at the hub site. An Eko-Core digital stethoscope^® with a web-based audio stream was available. Telehospitalists conducted encounters from a private office space with telehealth capabilities. A total of $9,000 was spent on equipment at both sites. Due to connectivity problems and data limits, the tablets were switched to mobile computer-on-wheels workstations and hospital-based Wi-Fi for the sustainability phase.

THS Description

An experienced hub hospitalist, together with an advanced practice provider (APP; nurse practitioner [NP] or physician assistant [PA]), cared for all patients admitted to the 10-bed inpatient unit at the spoke site, Monday through Friday from 8:00 AM to 4:30 PM. The APP had limited or no prior experience in acute inpatient medicine. The telehospitalist worked as a team with the APP. The APP was the main point of contact for nurses, performed physical examinations, and directed patient care to their level of comfort (in a similar manner as a teaching team). The telehospitalist conducted bedside patient rounds, participated in multidisciplinary huddles, and shared clinical documentation and administrative duties with the APP. The telehospitalist was the primary staff for admitted patients and had full access to the electronic health record (EHR). The THS was staffed by 10 hospitalists during the study period. Overnight and weekend cross-coverage and admissions were performed by MODs, who also covered the urgent care and cross-covered other nonmedical units.

Quantitative Evaluation Methods

Workload and Clinical Outcomes

An EHR query identified all patients admitted during the pre- and postimplementation periods. Demographic data, clinical Nosos risk scores,^26,27 and top admission diagnoses were reported. Workload was evaluated using the average number of encounters per day and self-reported telehospitalist worksheets, which were cross-referenced with EHR data. Clinical outcomes included LOS, 30-day hospital readmission rate, 30-day standardized mortality (SMR30), in-hospital mortality, and VHA-specific inpatient quality metrics. Independent sample t tests for continuous variables and chi-square tests or Fisher’s exact test (for patient class) for categorical variables were used to compare pre- and postimplementation groups. Statistical process control (SPC) charts evaluated changes over time. All analyses were conducted using Microsoft Excel and R.²⁸

Provider Satisfaction

Anonymous surveys were distributed to spoke-site inpatient and administrative staff at 1 month and 12 months postimplementation, assessing satisfaction, technology/connectivity, communication, and challenges (Appendix Figure 1). Satisfaction of the telehospitalist physicians at the hub site was measured 12 months postimplementation by a 26-question survey assessing the same domains, plus quality of care (Appendix Figure 2).

Patient Satisfaction

The VHA Survey of Healthcare Experiences of Patients (SHEP), a version of the Hospital Consumer Assessment of Healthcare Providers and Systems Survey,^29,30 was mailed to all patients after discharge. Survey responses concerning inpatient provider care (eg, care coordination, communication, hospital rating, willingness to recommend the hospital) during the pre- and postimplementation phases were compared using a two-sample test of independent proportions. Responses obtained during May and June 2019 were excluded.

Qualitative Evaluation Methods

The qualitative researcher observed information-sharing meetings and facilitated unstructured interviews with clinical and administrative staff during site visits preimplementation and 3 months after implementation. Interviews with administrators and clinical staff addressed their experiences with the THS, staff’s perception of patient and family response to THS implementation, administrative impacts, challenges, and strengths. All interviews and meetings were documented with handwritten notes and audio recordings. Interview summary notes were typed into a Microsoft Word document, verified by the physician-investigator, and synthesized by inductive themes into site-visit reports. Audio recordings were uploaded to a secure computer, transcribed, and reviewed for accuracy. The qualitative researcher also identified emerging themes from open-ended survey responses. Process evaluation findings were shared with administration at the spoke site.

The authors had full access to, and took full responsibility for, the integrity of the data. The project was evaluated by the University of Iowa Institutional Review Board and the Iowa City VA Research and Development Committee and was determined to be a non–human-subjects QI project.

Quantitative Workload and Clinical Outcomes

There were 822 admissions during the preimplementation period and 550 admissions during the postimplementation period (253 during the pilot and 297 during sustainability phase). Patient characteristics pre- and postimplementation were not significantly different (Table 1). The median patient age was 65 years; 96% of patients were male, and 83% were rural residents. The most common admission diagnosis was alcohol-related (36%); regarding patient disposition, 78% of admissions were discharged home.

Workload

There were 502 patient encounters staffed by the telehospitalist in the pilot phase, with an average of 6.25 encounters per day, and a telehospitalist-reported workload of 7 hours per day. There were 538 patient encounters, with an average of 4.67 encounters per day and a workload of 5.6 hours per day in the sustainability phase. The average daily census decreased from 5.0 (SD, 1.1) patients per day during preimplementation to 3.1 (SD, 0.5) patients per day during postimplementation (Table 2). In some of the months during the study period, admissions decreased below the lower SPC limit, suggesting a significant change (Figure). Adjusted LOS was significantly lower, with 3.0 (SD, 0.7) days vs 2.3 (SD, 0.3) days in the pre- and postimplementation periods, respectively. Bed occupancy rates were significantly lower in the sustainability phase compared with the pilot phase and the preimplementation period. Readmission rates varied, ranging from <10% to >30%, not significantly different but slightly higher in the postimplementation period. Readmission rates for heart failure, chronic obstructive pulmonary disease, and pneumonia remained unchanged; other medical readmissions (mostly alcohol-related) were slightly higher in the postimplementation period.

In-hospital mortality and SMR30 did not change significantly, but there was improvement in the 12-month rolling average of the observed/expected SMR30 from 1.40 to 1.08. Additional VHA-specific quality metrics were monitored and showed either small improvements or no change (data not shown).

Satisfaction at Hub and Spoke Sites

After sending two reminder communications via email, the telehospitalist satisfaction survey had a total response rate of 90% (9/10). Telehospitalists were satisfied or very satisfied (89%) with the program and the local providers (88.9%), rating their experience as good or excellent (100%) (Table 3). Communication with patients, families, and local staff was noted as being “positive” or “mostly positive.” Telehospitalists reported confidence in the accuracy of their diagnoses and rated the quality of care as being equal to that of a face-to-face encounter. Connectivity problems were prevalent, although most providers were able to resort to a back-up plan. Other challenges included differences in culture and concerns about liability. We received 27 responses from the spoke-site satisfaction survey; the response rate could not be determined because the survey was distributed by the spoke site for anonymity. Of the respondents, 37% identified as nurses, 25.9% as healthcare providers (APPs or physicians), and 33.3% as other staff (eg, social worker, nutritionist, physical therapist, utilization management, administrators); 3.7% did not respond. Among the participants, 88% had personally interacted with the THS. Most providers and other staff perceived THS as valuable (57.1% and 77.8%, respectively) and were satisfied or highly satisfied with THS (57.1% and 55.6%, respectively). On average, nurses provided lower ratings across all survey items than providers and other staff. Challenges noted by all staff included issues with communication, workflow, and technology/connectivity.

Regarding patient satisfaction, the SHEP survey showed a significant improvement in care coordination (18%; P = .02) and a nonsignificant improvement in communications about medications (5%; P =.054). The remaining items in the survey, including overall hospital rating and willingness to recommend the hospital, were unchanged (Appendix Table).

Qualitative Strengths

Our process evaluation identified high quality of care and teamwork as contributors to the success of the program. Overall, staff credited perceived improvements in quality of care to the quality of providers staffing the THS, including the local APPs. Noting the telehospitalists’ knowledge base and level of engagement as key attributes, one staff member commented: “I prefer a telehospitalist that really care[s] about patients than some provider that is physically here but does not engage.” Staff perceived improvements in the continuity of care, as well as care processes such as handoffs and transitions of care.

Improvements in teamwork were perceived compared with the previous model of care. Telehospitalists were lauded for their professionalism and communication skills. Overall, nurses felt providers in the THS listened more to their views. In addition, nurse respondents felt they could learn from several providers and said they enjoyed the telehospitalists’ disposition to teach and discuss patient care. The responsiveness of the THS staff was instrumental in building teamwork and acceptance. A bedside interdisciplinary protocol was established for appropriate patients. Local staff felt this was crucial for teamwork and patient satisfaction. Telehospitalists reported high-value in interdisciplinary rounds, facilitating interaction with nurses and ancillary staff. Handoff problems were identified, leading to QI initiatives to mitigate those issues.

Challenges

The survey identified administrative barriers, technical difficulties, workflow constraints, and clinical concerns. The credentialing process was complicated, delaying the onboarding of telehospitalists. Internet connectivity was inconsistent, leading to disruption in video communications; however, during the sustainability phase, updated technology improved communications. The communication workflow was resisted by some nurses, who wanted to phone the telehospitalist directly rather than having the local APP as the first contact. Secure messaging was enabled to allow nurses direct contact during the sustainability phase.

Workload was a concern among telehospitalists and local staff. Telehospitalists perceived the documentation requirements and administrative workload to be two to three times higher than at other hospitals—despite the lower number of encounters. Finally, clinical concerns from spoke-site clinicians included a perceived rise in the acuity of patients (which was not evident by the Nosos score) and delayed decisions to transfer-out patients. These concerns were addressed with educational sessions for telehospitalists during the sustainability phase.

Additional Quality Improvement Projects

The implementation of THS resulted in QI initiatives at the spoke site, including an EHR-integrated handoff tool; a documentation evaluation that led to the elimination of duplicative, inefficient, and error-prone templates; and a revision of the alcohol withdrawal treatment protocol during the sustainability phase to reduce the use of intravenous benzodiazepines. A more comprehensive benzodiazepine-sparing alcohol withdrawal treatment protocol was also developed but was not implemented until after the study period (January 2020).

Our pre-post study evaluation found implementation of a THS to be noninferior to face-to-face care, with no significant change in mortality, readmission rate, or patient satisfaction. The significant improvement observed in LOS is consistent with the adoption of hospitalist models in other medical care settings,¹¹ but had not been reported by previous telehospitalist studies. For example, in their retrospective chart review comparing an NP-supported telehospitalist model to locum tenens hospitalists, Boltz et al found no difference in LOS.³¹ Moreover, as in our study, they found no differences in readmissions, mortality, and patient satisfaction.³¹ Similarly, Kuperman et al reported unchanged daily census, LOS, and transfer rates from a CAH with their virtual hospitalist program, but a decrease in the percentage of patients transferred-out from the emergency department, suggesting that more patients were treated locally.¹⁹

Reduction in LOS is one of the primary measures of efficiency in hospital care³¹; reducing LOS while maintaining the quality of care lowers hospital costs. The reduction in LOS in our study could be attributed to greater continuity of care, engagement/experience of the telehospitalists, or other factors. This decrease in LOS and slight reduction in admissions resulted in an overall lower daily census during the study period and impacted efficiency. Our study was unable to determine the cause for the reduction in admissions; however, several concurrent events, including the expansion of community-care options for veterans under the MISSION ACT (Maintaining Internal Systems and Strengthening Integrated Outside Networks Act) in June 2019, a nationwide smoking ban at VA facilities (October 2019), and a modification in the alcohol withdrawal treatment protocol might have influenced veterans’ choice of hospital.

Readmission rates were slightly higher, though nonsignificant, in the postimplementation period. Alcohol-related readmissions accounted for most readmissions; some of the protocol changes, such as admitting all patients with alcohol withdrawal to inpatient class instead of admitting some to the observation class, accounted for part of the increase in readmission rates. Readmission rates for other conditions such as chronic obstructive pulmonary disease, chronic heart failure, or pneumonia were not significantly different, suggesting that the reduction in LOS did not result in an unintended increased readmission rate for those conditions.

Rural hospitals are struggling with staffing and finances. Resorting to locum tenens staffing is costly and can result in variable quality of care.^32,33 APPs are increasingly taking on hospitalist positions, with 65% of adult hospitalist programs, including half of all VHA hospitals, employing NPs and PAs.^34,35 In response to this expanded scope of practice, hospitals employing APPs in hospitalist roles must comply with state and federal laws, which often require that APPs be supervised by or work in collaboration with an on-site or off-site physician. The THS is a great model to support APPs and address staffing and cost challenges in low-volume rural facilities, while maintaining quality of care. Some APP-telehospitalist programs similar to ours have reported cost reductions of up 58% compared to programs that employ locum tenens physicians.³¹ In our model, we assume that a single telehospitalist hub could provide coverage to two or three spoke sites with APP support, reducing staffing costs.

Hub telehospitalists reported satisfaction with the program, and they perceived the quality of care to be comparable to face-to-face encounters; their responses were consistent with those previously reported in an evaluation of telemedicine acute care by JaKa et al.²⁰ Spoke-site staff, however, had a mixed level of satisfaction, which was different from responses reported by JaKa et al.²⁰ The primary challenges encountered were technological and communication issues, differences in cultures of care between the hub and the spoke sites, and buy-in from frontline staff. Differences in expectations and unclear role definitions between the local APP and the telehospitalist were identified as contributors to dissatisfaction with the program by the nursing staff. Modifications to the communication processes between nurses and telehospitalists and role clarification improved the experience. Culture and practice differences between spoke physicians and the telehospitalist persisted throughout the program implementation, and likely affected the hub providers’ perception of the THS. This was evidenced by reluctance from spoke physicians to implement warm handoffs or participate in THS meetings and resistance to protocol changes. Additional evaluations, collaborations. and interventions are required to improve satisfaction of spoke-site staff.

This study has several limitations. First, the VHA is an integrated health system, one that serves an older, predominantly male patient population. Also, the lack of reimbursement and interstate licensing restrictions limit generalizability of these results to other CAHs or healthcare systems. Furthermore, the intervention was limited to a single rural site; while this allowed for a detailed evaluation, unique barriers or facilitators might exist that limit its applicability. In addition, QI initiatives implemented by the VHA during the project period might have confounded some of our results. Last, patient satisfaction survey data are overall limited in their ability to fully assess patient’s experience and satisfaction with the program. Further qualitative studies are needed to gain deeper insight into patient perspectives with the THS and whether modality of care delivery influences patients’ care decisions. Future studies should consider a multisite design with one or more hubs and multiple spoke sites.

Telehospitalist services are a feasible and safe approach to provide inpatient services and address staffing needs of rural hospitals. To enhance program performance, it is essential to ensure adequate technological quality, clearly delineate and define roles and responsibilities of the care team, and address communication issues or staff concerns in a timely manner.

The authors thank the staff, administration, and leadership at the Tomah and Iowa City VA Medical Centers for working with us on this project. They offer special thanks to Kevin Glenn, MD, MS, Ethan Kuperman, MD, MS, FHM, and Jennifer Chapin, MSN, RN, for sharing their expertise, and the telehealth team, including Nathaniel Samuelson, Angela McDowell, and Katrin Metcalf.

The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.