Editorial

Prolonged bedrest with minimum mobility is associated with worse outcomes for hospitalized patients, particularly the elderly.^1,2 Immobility accelerates loss of independent function and leads to complications such as deep vein thrombosis, pressure ulcers, and even death.^3,4 Increasing activity and mobility early in hospitalization, even among critically ill patients, has proven safe.⁵ Patients with intravascular devices, urinary catheters, and even those requiring mechanical ventilation or extracorporeal membranous oxygenation can safely perform exercise and out-of-bed activities.⁵

Although the remedy for immobility and bedrest seems obvious, implementing workflows and strategies to increase inpatient mobility has proven challenging. Physical therapists—often the first solution considered to mobilize patients—are a limited resource and are often coordinating with other team members on care planning activities such as facilitating discharge, arranging for equipment, and educating patients and families, rather than assisting with routine mobility needs.⁶ Nurses share responsibility for patient activity, but they also have broad patient-care responsibilities competing for their time.⁷ Additionally, some nurses may feel they do not have the necessary training to safely mobilize patients.^8,9

In this context, the work by Rothberg et al. is a welcome addition to the literature. In this single-blind randomized pilot trial, 102 inpatients aged 60 years and older were randomly assigned to either of two groups: intervention (ambulation protocol) or usual care. In the intervention arm, dedicated mobility technicians—ie, redeployed patient-care nursing assistants trained in safe patient-handling practices—were tasked to help patients walk three times daily. Patients in the intervention group took significantly more steps on average compared with those receiving usual care (994 versus 668). Additionally, patients with greater exposure to the mobility technicians (>2 days) had significantly higher step counts and were more likely to achieve >900 steps per day, below which patients are likely to experience functional decline.¹⁰ This study highlights the feasibility of using trained mobility technicians rather than more expensive providers (eg, physical therapists, occupational therapists, or nurses) to enhance inpatient ambulation.

The authors confirmed previously known findings that inpatient mobility, which was assessed in this study by accelerometers, predicts post-hospital patient disposition. Although consumer grade accelerometer devices (eg, Fitbit^©), have limitations and may not count steps accurately for hospitalized patients who walk slowly or have gait abnormalities,¹¹ Rothberg et al. still found that higher step count was associated with discharge home rather than to a facility. Discharge planning in the hospital is often delayed because clinicians fail to recognize impaired mobility until after resolution of acute medical/surgical issues.¹² The use of routinely collected mobility measurements, such as step count, to inform decisions around care coordination and discharge planning may ultimately prove helpful for hospital throughput.

Despite the increased mobility observed in the intervention group, discharge disposition after hospitalization and hospital length of stay (LOS) did not differ between groups, whether analyzed according to per-protocol or intention-to-treat analysis. Although LOS and discharge disposition are known to be associated with patient functional status, they are also influenced by other factors, such as social support, health insurance, medical status, and patient or family preferences.^13-16 Furthermore, illness severity may confound the association between step count and outcomes: sicker patients walk less, stay longer, and are more likely to need postacute rehabilitation. Thus, the effect size of a mobility intervention may be smaller than expected based on observational data, leading to underpowering. Another possibility is that the intervention did not affect these clinical outcomes because patients in the intervention group only received the intervention for an average of one-third of their hospitalization period and the mobility goal of three times per day was not consistently achieved. Mobility technician involvement was often delayed because the study required physical therapy evaluations to determine patient appropriateness before the mobility intervention was initiated. This aspect of study design belies a commonplace cultural practice to defer inpatient mobilization until a physical therapist has first evaluated the patient. Moreover, limiting mobility interventions to a single provider, such as a mobility technician, can mean that patients are less likely to be mobilized if that resource is not available. Establishing an interdisciplinary culture of mobility is more likely to be successful.¹⁷ One possible strategy is to start with nurse-performed systematic assessments of functional ability to set daily mobility goals that any appropriate provider, including a mobility technician, could help to implement.^18,19

Although studies designed to increase hospital mobility have yielded mixed results,²⁰ and larger high-quality clinical trials are needed to demonstrate clear and consistent benefits on patient-centered and operational outcomes, we applaud research and quality improvement efforts (including the current study) that promote inpatient mobility through strategies and measurements that do not require intensive physical therapist involvement. Mobility technicians may represent one step forward in enhancing a culture of mobility.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated.

Prolonged bedrest with minimum mobility is associated with worse outcomes for hospitalized patients, particularly the elderly.^1,2 Immobility accelerates loss of independent function and leads to complications such as deep vein thrombosis, pressure ulcers, and even death.^3,4 Increasing activity and mobility early in hospitalization, even among critically ill patients, has proven safe.⁵ Patients with intravascular devices, urinary catheters, and even those requiring mechanical ventilation or extracorporeal membranous oxygenation can safely perform exercise and out-of-bed activities.⁵

Although the remedy for immobility and bedrest seems obvious, implementing workflows and strategies to increase inpatient mobility has proven challenging. Physical therapists—often the first solution considered to mobilize patients—are a limited resource and are often coordinating with other team members on care planning activities such as facilitating discharge, arranging for equipment, and educating patients and families, rather than assisting with routine mobility needs.⁶ Nurses share responsibility for patient activity, but they also have broad patient-care responsibilities competing for their time.⁷ Additionally, some nurses may feel they do not have the necessary training to safely mobilize patients.^8,9

In this context, the work by Rothberg et al. is a welcome addition to the literature. In this single-blind randomized pilot trial, 102 inpatients aged 60 years and older were randomly assigned to either of two groups: intervention (ambulation protocol) or usual care. In the intervention arm, dedicated mobility technicians—ie, redeployed patient-care nursing assistants trained in safe patient-handling practices—were tasked to help patients walk three times daily. Patients in the intervention group took significantly more steps on average compared with those receiving usual care (994 versus 668). Additionally, patients with greater exposure to the mobility technicians (>2 days) had significantly higher step counts and were more likely to achieve >900 steps per day, below which patients are likely to experience functional decline.¹⁰ This study highlights the feasibility of using trained mobility technicians rather than more expensive providers (eg, physical therapists, occupational therapists, or nurses) to enhance inpatient ambulation.

The authors confirmed previously known findings that inpatient mobility, which was assessed in this study by accelerometers, predicts post-hospital patient disposition. Although consumer grade accelerometer devices (eg, Fitbit^©), have limitations and may not count steps accurately for hospitalized patients who walk slowly or have gait abnormalities,¹¹ Rothberg et al. still found that higher step count was associated with discharge home rather than to a facility. Discharge planning in the hospital is often delayed because clinicians fail to recognize impaired mobility until after resolution of acute medical/surgical issues.¹² The use of routinely collected mobility measurements, such as step count, to inform decisions around care coordination and discharge planning may ultimately prove helpful for hospital throughput.

Despite the increased mobility observed in the intervention group, discharge disposition after hospitalization and hospital length of stay (LOS) did not differ between groups, whether analyzed according to per-protocol or intention-to-treat analysis. Although LOS and discharge disposition are known to be associated with patient functional status, they are also influenced by other factors, such as social support, health insurance, medical status, and patient or family preferences.^13-16 Furthermore, illness severity may confound the association between step count and outcomes: sicker patients walk less, stay longer, and are more likely to need postacute rehabilitation. Thus, the effect size of a mobility intervention may be smaller than expected based on observational data, leading to underpowering. Another possibility is that the intervention did not affect these clinical outcomes because patients in the intervention group only received the intervention for an average of one-third of their hospitalization period and the mobility goal of three times per day was not consistently achieved. Mobility technician involvement was often delayed because the study required physical therapy evaluations to determine patient appropriateness before the mobility intervention was initiated. This aspect of study design belies a commonplace cultural practice to defer inpatient mobilization until a physical therapist has first evaluated the patient. Moreover, limiting mobility interventions to a single provider, such as a mobility technician, can mean that patients are less likely to be mobilized if that resource is not available. Establishing an interdisciplinary culture of mobility is more likely to be successful.¹⁷ One possible strategy is to start with nurse-performed systematic assessments of functional ability to set daily mobility goals that any appropriate provider, including a mobility technician, could help to implement.^18,19

Although studies designed to increase hospital mobility have yielded mixed results,²⁰ and larger high-quality clinical trials are needed to demonstrate clear and consistent benefits on patient-centered and operational outcomes, we applaud research and quality improvement efforts (including the current study) that promote inpatient mobility through strategies and measurements that do not require intensive physical therapist involvement. Mobility technicians may represent one step forward in enhancing a culture of mobility.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated.

Prolonged bedrest with minimum mobility is associated with worse outcomes for hospitalized patients, particularly the elderly.^1,2 Immobility accelerates loss of independent function and leads to complications such as deep vein thrombosis, pressure ulcers, and even death.^3,4 Increasing activity and mobility early in hospitalization, even among critically ill patients, has proven safe.⁵ Patients with intravascular devices, urinary catheters, and even those requiring mechanical ventilation or extracorporeal membranous oxygenation can safely perform exercise and out-of-bed activities.⁵

Although the remedy for immobility and bedrest seems obvious, implementing workflows and strategies to increase inpatient mobility has proven challenging. Physical therapists—often the first solution considered to mobilize patients—are a limited resource and are often coordinating with other team members on care planning activities such as facilitating discharge, arranging for equipment, and educating patients and families, rather than assisting with routine mobility needs.⁶ Nurses share responsibility for patient activity, but they also have broad patient-care responsibilities competing for their time.⁷ Additionally, some nurses may feel they do not have the necessary training to safely mobilize patients.^8,9

In this context, the work by Rothberg et al. is a welcome addition to the literature. In this single-blind randomized pilot trial, 102 inpatients aged 60 years and older were randomly assigned to either of two groups: intervention (ambulation protocol) or usual care. In the intervention arm, dedicated mobility technicians—ie, redeployed patient-care nursing assistants trained in safe patient-handling practices—were tasked to help patients walk three times daily. Patients in the intervention group took significantly more steps on average compared with those receiving usual care (994 versus 668). Additionally, patients with greater exposure to the mobility technicians (>2 days) had significantly higher step counts and were more likely to achieve >900 steps per day, below which patients are likely to experience functional decline.¹⁰ This study highlights the feasibility of using trained mobility technicians rather than more expensive providers (eg, physical therapists, occupational therapists, or nurses) to enhance inpatient ambulation.

The authors confirmed previously known findings that inpatient mobility, which was assessed in this study by accelerometers, predicts post-hospital patient disposition. Although consumer grade accelerometer devices (eg, Fitbit^©), have limitations and may not count steps accurately for hospitalized patients who walk slowly or have gait abnormalities,¹¹ Rothberg et al. still found that higher step count was associated with discharge home rather than to a facility. Discharge planning in the hospital is often delayed because clinicians fail to recognize impaired mobility until after resolution of acute medical/surgical issues.¹² The use of routinely collected mobility measurements, such as step count, to inform decisions around care coordination and discharge planning may ultimately prove helpful for hospital throughput.

Despite the increased mobility observed in the intervention group, discharge disposition after hospitalization and hospital length of stay (LOS) did not differ between groups, whether analyzed according to per-protocol or intention-to-treat analysis. Although LOS and discharge disposition are known to be associated with patient functional status, they are also influenced by other factors, such as social support, health insurance, medical status, and patient or family preferences.^13-16 Furthermore, illness severity may confound the association between step count and outcomes: sicker patients walk less, stay longer, and are more likely to need postacute rehabilitation. Thus, the effect size of a mobility intervention may be smaller than expected based on observational data, leading to underpowering. Another possibility is that the intervention did not affect these clinical outcomes because patients in the intervention group only received the intervention for an average of one-third of their hospitalization period and the mobility goal of three times per day was not consistently achieved. Mobility technician involvement was often delayed because the study required physical therapy evaluations to determine patient appropriateness before the mobility intervention was initiated. This aspect of study design belies a commonplace cultural practice to defer inpatient mobilization until a physical therapist has first evaluated the patient. Moreover, limiting mobility interventions to a single provider, such as a mobility technician, can mean that patients are less likely to be mobilized if that resource is not available. Establishing an interdisciplinary culture of mobility is more likely to be successful.¹⁷ One possible strategy is to start with nurse-performed systematic assessments of functional ability to set daily mobility goals that any appropriate provider, including a mobility technician, could help to implement.^18,19

Although studies designed to increase hospital mobility have yielded mixed results,²⁰ and larger high-quality clinical trials are needed to demonstrate clear and consistent benefits on patient-centered and operational outcomes, we applaud research and quality improvement efforts (including the current study) that promote inpatient mobility through strategies and measurements that do not require intensive physical therapist involvement. Mobility technicians may represent one step forward in enhancing a culture of mobility.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated.

Bacteremia is common and associated with significant morbidity and mortality. Bloodstream infections rank among the leading causes of death in North America and Europe.¹

See related article

In this issue, Mushtaq et al² contend that follow-up blood cultures after initial bacteremia are not needed for most hospitalized patients. Not repeating blood cultures after initial bacteremia has been proposed to decrease hospitalization length, consultations, and healthcare costs in some clinical settings. However, without follow-up cultures, it can be difficult to assess the adequacy of treatment of bacteremia and associated underlying infections.

GRAM-NEGATIVE ORGANISMS

Results of retrospective studies indicate that follow-up cultures may not be routinely needed for gram-negative bacteremia. In a review by Canzoneri et al of 383 cases with subsequent follow-up cultures,³ 55 (14%) were positive. The mean duration of bacteremia was 2.8 days (range 1 to 15 days). Of the 55 persistently positive blood cultures, only 8 (15%) were caused by gram-negative organisms. Limitations to this study included the lack of patient outcome data, a low event rate, and the retrospective design.⁴

In a retrospective case-control study of follow-up cultures for 862 episodes of Klebsiella pneumoniae bacteremia,⁵ independent risk factors for persistent bacteremia were intra-abdominal infection, higher Charlson comorbidity index score, solid-organ transplant, and unfavorable treatment response.

These studies confirm that persistent bacteremia is uncommon with gram-negative organisms. They also support using comorbidities and treatment response to guide the ordering of follow-up blood cultures.

WHEN IS FOLLOW-UP CULTURE USEFUL?

Although follow-up blood cultures may not be needed routinely in patients with gram- negative bacteremia, it would be difficult to extrapolate this to gram-positive organisms, especially Staphylococcus aureus.

In Canzoneri et al,³ 43 (78%) of the 55 positive follow-up cultures were due to gram-positive organisms. Factors associated with positive follow-up cultures were concurrent fever, presence of a central intravenous line, end-stage renal disease on hemodialysis, and diabetes mellitus. In addition, infectious disease consultation to decide the need for follow-up cultures for S aureus bacteremia has been associated with fewer deaths, fewer relapses, and lower readmission rates.^6,7

In certain clinical scenarios, follow-up blood cultures can provide useful information, such as when the source of bacteremia is endocarditis or cardiac device infection, a vascular graft, or an intravascular line. In the Infectious Diseases Society of America guidelines for diagnosis and management of catheter-related bloodstream infections, persistent or relapsing bacteremia for some organisms is a criterion for removal of a long-term central venous catheter.⁸

Follow-up cultures are especially useful when the focus of infection is protected from antibiotic penetration, such as in the central nervous system, joints, and abdominal or other abscess. These foci may require drainage for cure. In these cases or in the setting of unfavorable clinical treatment response, follow-up blood cultures showing persistent bacteremia can prompt a search for unaddressed or incompletely addressed foci of infection and allow for source control.

The timing of follow-up cultures is generally 1 to 2 days after the initial culture. Although Mushtaq et al propose a different approach, traditional teaching has been that the last blood culture should not be positive, and this leads to ordering follow-up blood cultures until clearance of bacteremia is documented.

Bacteremia is common and associated with significant morbidity and mortality. Bloodstream infections rank among the leading causes of death in North America and Europe.¹

See related article

In this issue, Mushtaq et al² contend that follow-up blood cultures after initial bacteremia are not needed for most hospitalized patients. Not repeating blood cultures after initial bacteremia has been proposed to decrease hospitalization length, consultations, and healthcare costs in some clinical settings. However, without follow-up cultures, it can be difficult to assess the adequacy of treatment of bacteremia and associated underlying infections.

GRAM-NEGATIVE ORGANISMS

Results of retrospective studies indicate that follow-up cultures may not be routinely needed for gram-negative bacteremia. In a review by Canzoneri et al of 383 cases with subsequent follow-up cultures,³ 55 (14%) were positive. The mean duration of bacteremia was 2.8 days (range 1 to 15 days). Of the 55 persistently positive blood cultures, only 8 (15%) were caused by gram-negative organisms. Limitations to this study included the lack of patient outcome data, a low event rate, and the retrospective design.⁴

In a retrospective case-control study of follow-up cultures for 862 episodes of Klebsiella pneumoniae bacteremia,⁵ independent risk factors for persistent bacteremia were intra-abdominal infection, higher Charlson comorbidity index score, solid-organ transplant, and unfavorable treatment response.

These studies confirm that persistent bacteremia is uncommon with gram-negative organisms. They also support using comorbidities and treatment response to guide the ordering of follow-up blood cultures.

WHEN IS FOLLOW-UP CULTURE USEFUL?

Although follow-up blood cultures may not be needed routinely in patients with gram- negative bacteremia, it would be difficult to extrapolate this to gram-positive organisms, especially Staphylococcus aureus.

In Canzoneri et al,³ 43 (78%) of the 55 positive follow-up cultures were due to gram-positive organisms. Factors associated with positive follow-up cultures were concurrent fever, presence of a central intravenous line, end-stage renal disease on hemodialysis, and diabetes mellitus. In addition, infectious disease consultation to decide the need for follow-up cultures for S aureus bacteremia has been associated with fewer deaths, fewer relapses, and lower readmission rates.^6,7

In certain clinical scenarios, follow-up blood cultures can provide useful information, such as when the source of bacteremia is endocarditis or cardiac device infection, a vascular graft, or an intravascular line. In the Infectious Diseases Society of America guidelines for diagnosis and management of catheter-related bloodstream infections, persistent or relapsing bacteremia for some organisms is a criterion for removal of a long-term central venous catheter.⁸

Follow-up cultures are especially useful when the focus of infection is protected from antibiotic penetration, such as in the central nervous system, joints, and abdominal or other abscess. These foci may require drainage for cure. In these cases or in the setting of unfavorable clinical treatment response, follow-up blood cultures showing persistent bacteremia can prompt a search for unaddressed or incompletely addressed foci of infection and allow for source control.

The timing of follow-up cultures is generally 1 to 2 days after the initial culture. Although Mushtaq et al propose a different approach, traditional teaching has been that the last blood culture should not be positive, and this leads to ordering follow-up blood cultures until clearance of bacteremia is documented.

Bacteremia is common and associated with significant morbidity and mortality. Bloodstream infections rank among the leading causes of death in North America and Europe.¹

See related article

In this issue, Mushtaq et al² contend that follow-up blood cultures after initial bacteremia are not needed for most hospitalized patients. Not repeating blood cultures after initial bacteremia has been proposed to decrease hospitalization length, consultations, and healthcare costs in some clinical settings. However, without follow-up cultures, it can be difficult to assess the adequacy of treatment of bacteremia and associated underlying infections.

GRAM-NEGATIVE ORGANISMS

Results of retrospective studies indicate that follow-up cultures may not be routinely needed for gram-negative bacteremia. In a review by Canzoneri et al of 383 cases with subsequent follow-up cultures,³ 55 (14%) were positive. The mean duration of bacteremia was 2.8 days (range 1 to 15 days). Of the 55 persistently positive blood cultures, only 8 (15%) were caused by gram-negative organisms. Limitations to this study included the lack of patient outcome data, a low event rate, and the retrospective design.⁴

In a retrospective case-control study of follow-up cultures for 862 episodes of Klebsiella pneumoniae bacteremia,⁵ independent risk factors for persistent bacteremia were intra-abdominal infection, higher Charlson comorbidity index score, solid-organ transplant, and unfavorable treatment response.

These studies confirm that persistent bacteremia is uncommon with gram-negative organisms. They also support using comorbidities and treatment response to guide the ordering of follow-up blood cultures.

WHEN IS FOLLOW-UP CULTURE USEFUL?

Although follow-up blood cultures may not be needed routinely in patients with gram- negative bacteremia, it would be difficult to extrapolate this to gram-positive organisms, especially Staphylococcus aureus.

In Canzoneri et al,³ 43 (78%) of the 55 positive follow-up cultures were due to gram-positive organisms. Factors associated with positive follow-up cultures were concurrent fever, presence of a central intravenous line, end-stage renal disease on hemodialysis, and diabetes mellitus. In addition, infectious disease consultation to decide the need for follow-up cultures for S aureus bacteremia has been associated with fewer deaths, fewer relapses, and lower readmission rates.^6,7

In certain clinical scenarios, follow-up blood cultures can provide useful information, such as when the source of bacteremia is endocarditis or cardiac device infection, a vascular graft, or an intravascular line. In the Infectious Diseases Society of America guidelines for diagnosis and management of catheter-related bloodstream infections, persistent or relapsing bacteremia for some organisms is a criterion for removal of a long-term central venous catheter.⁸

Follow-up cultures are especially useful when the focus of infection is protected from antibiotic penetration, such as in the central nervous system, joints, and abdominal or other abscess. These foci may require drainage for cure. In these cases or in the setting of unfavorable clinical treatment response, follow-up blood cultures showing persistent bacteremia can prompt a search for unaddressed or incompletely addressed foci of infection and allow for source control.

The timing of follow-up cultures is generally 1 to 2 days after the initial culture. Although Mushtaq et al propose a different approach, traditional teaching has been that the last blood culture should not be positive, and this leads to ordering follow-up blood cultures until clearance of bacteremia is documented.

Reduction of antibiotic overuse is an important goal for improving the quality of care for children and is highlighted in many of the Choosing Wisely^® recommendations across disciplines.^1-3 However, the evidence supporting these recommendations vary widely and many are derived from expert opinion and clinical practice guidelines rather than from original research studies.² In this issue of the Journal of Hospital Medicine, Schroeder and colleagues identify a potential area of antibiotic overuse among young febrile infants with possible urinary tract infection (UTI).⁴ A wide variation in antibiotic treatment rates (0%-35%) was observed across 124 hospitals in the United States for febrile infants 7-60 days of age with uropathogen detection by urine culture but a negative urinalysis (UA). Treated infants with a negative UA were more likely to be younger (7-30 days), have respiratory symptoms, and were less likely to have abnormal inflammatory markers than infants with a positive UA.

Clinicians faced with the decision of whether or not to treat a febrile infant with uropathogen detection in the setting of a negative UA must weigh the potential benefits and harms of antibiotic use in this population. Withholding antibiotics for a young infant with UTI may increase the risk of recurrent UTI and renal scarring,^5,6 while antibiotic treatment in young infants can lead to the disruption of the gut microbiome, resulting in long-term consequences that are only beginning to be understood.^7-10

The American Academy of Pediatrics (AAP) UTI practice parameter requires a positive UA to establish the diagnosis of UTI in children 2-24 months of age.¹¹ This recommendation is based primarily on studies demonstrating that uropathogen detection in the setting of a negative UA commonly represents asymptomatic bacteriuria or contamination rather than true infection.^12-14 This is supported by research showing that the UA demonstrates near perfect (>99%) sensitivity for UTI in children with bacteremic UTI,^12,15 and studies demonstrating lower rates of subsequent urinary infections and renal injury among infants with uropathogen detection and a negative UA compared with those with uropathogen detection and a positive UA.^13,14,16

An important question is whether febrile infants within the first two months of life with uropathogen detection should be treated with antimicrobials regardless of UA findings or specifically in the setting of a negative UA. The AAP practice guideline¹¹ deliberately omits these young infants, recognizing that evidence derived from studies of older infants and children may not be applicable to this young age group, as they may not mount as robust an inflammatory response and thus may not demonstrate pyuria in the setting of a bacterial urinary infection. Schroeder et al. demonstrate lower rates of abnormal inflammatory markers in UA negative compared with UA positive infants, a finding the authors argue supports the possibility of asymptomatic bacteriuria or contamination rather than true infection.⁴ The counterargument is that young infants may not mount a significant inflammatory response to true infections.

The authors appropriately highlight the paucity of literature to help differentiate true infection from asymptomatic bacteriuria or contamination in infants less than two months of age. As infants in this age group are usually treated with antibiotics for a positive urine culture regardless of UA result, robust data on short- and long-term outcomes of untreated infants are lacking. Much of the existing literature evaluates the test performance of the UA for UTI using the urine culture as the reference standard, which presents inherent limitations with incorporating the results of the UA into the definition of UTI using these data. Additionally, reported test performance of the UA for UTI varies by uropathogen type,¹⁷ fever duration,¹⁸ associated bacteremia,¹⁹ and urine concentration,²⁰ which are important considerations when applying a strict definition of UTI that includes the UA in this age group. Conversely, more recent studies have demonstrated improved sensitivity of the dipstick and microscopic UA for the detection of UTI.^15,20,21 The improved test performance may not only enhance the use of the UA as a screen for UTI in this high-risk population but also allow its potential inclusion into the definition of UTI as the authors suggest, as previous false-negative UTIs would be less frequent with improved UA testing modalities.

Ultimately, what’s missing from the equation is whether treatment of young febrile infants with uropathogen detection in the setting of a negative UA affects either short-term or long-term complications of UTI. Unfortunately, limited information exists to help inform the decision to initiate antibiotic treatment for these infants. Ideally, this question can only be answered by either an observational study evaluating outcomes of untreated infants or a randomized trial of antibiotics for infants less than two months of age with uropathogen detection in the setting of a negative UA. Until then, we may continue to observe a wide variation in antibiotic treatment rates for febrile young infants with uropathogen detection in the setting of a negative UA.

Disclosures

The authors have nothing to disclose.

Reduction of antibiotic overuse is an important goal for improving the quality of care for children and is highlighted in many of the Choosing Wisely^® recommendations across disciplines.^1-3 However, the evidence supporting these recommendations vary widely and many are derived from expert opinion and clinical practice guidelines rather than from original research studies.² In this issue of the Journal of Hospital Medicine, Schroeder and colleagues identify a potential area of antibiotic overuse among young febrile infants with possible urinary tract infection (UTI).⁴ A wide variation in antibiotic treatment rates (0%-35%) was observed across 124 hospitals in the United States for febrile infants 7-60 days of age with uropathogen detection by urine culture but a negative urinalysis (UA). Treated infants with a negative UA were more likely to be younger (7-30 days), have respiratory symptoms, and were less likely to have abnormal inflammatory markers than infants with a positive UA.

Clinicians faced with the decision of whether or not to treat a febrile infant with uropathogen detection in the setting of a negative UA must weigh the potential benefits and harms of antibiotic use in this population. Withholding antibiotics for a young infant with UTI may increase the risk of recurrent UTI and renal scarring,^5,6 while antibiotic treatment in young infants can lead to the disruption of the gut microbiome, resulting in long-term consequences that are only beginning to be understood.^7-10

The American Academy of Pediatrics (AAP) UTI practice parameter requires a positive UA to establish the diagnosis of UTI in children 2-24 months of age.¹¹ This recommendation is based primarily on studies demonstrating that uropathogen detection in the setting of a negative UA commonly represents asymptomatic bacteriuria or contamination rather than true infection.^12-14 This is supported by research showing that the UA demonstrates near perfect (>99%) sensitivity for UTI in children with bacteremic UTI,^12,15 and studies demonstrating lower rates of subsequent urinary infections and renal injury among infants with uropathogen detection and a negative UA compared with those with uropathogen detection and a positive UA.^13,14,16

An important question is whether febrile infants within the first two months of life with uropathogen detection should be treated with antimicrobials regardless of UA findings or specifically in the setting of a negative UA. The AAP practice guideline¹¹ deliberately omits these young infants, recognizing that evidence derived from studies of older infants and children may not be applicable to this young age group, as they may not mount as robust an inflammatory response and thus may not demonstrate pyuria in the setting of a bacterial urinary infection. Schroeder et al. demonstrate lower rates of abnormal inflammatory markers in UA negative compared with UA positive infants, a finding the authors argue supports the possibility of asymptomatic bacteriuria or contamination rather than true infection.⁴ The counterargument is that young infants may not mount a significant inflammatory response to true infections.

The authors appropriately highlight the paucity of literature to help differentiate true infection from asymptomatic bacteriuria or contamination in infants less than two months of age. As infants in this age group are usually treated with antibiotics for a positive urine culture regardless of UA result, robust data on short- and long-term outcomes of untreated infants are lacking. Much of the existing literature evaluates the test performance of the UA for UTI using the urine culture as the reference standard, which presents inherent limitations with incorporating the results of the UA into the definition of UTI using these data. Additionally, reported test performance of the UA for UTI varies by uropathogen type,¹⁷ fever duration,¹⁸ associated bacteremia,¹⁹ and urine concentration,²⁰ which are important considerations when applying a strict definition of UTI that includes the UA in this age group. Conversely, more recent studies have demonstrated improved sensitivity of the dipstick and microscopic UA for the detection of UTI.^15,20,21 The improved test performance may not only enhance the use of the UA as a screen for UTI in this high-risk population but also allow its potential inclusion into the definition of UTI as the authors suggest, as previous false-negative UTIs would be less frequent with improved UA testing modalities.

Ultimately, what’s missing from the equation is whether treatment of young febrile infants with uropathogen detection in the setting of a negative UA affects either short-term or long-term complications of UTI. Unfortunately, limited information exists to help inform the decision to initiate antibiotic treatment for these infants. Ideally, this question can only be answered by either an observational study evaluating outcomes of untreated infants or a randomized trial of antibiotics for infants less than two months of age with uropathogen detection in the setting of a negative UA. Until then, we may continue to observe a wide variation in antibiotic treatment rates for febrile young infants with uropathogen detection in the setting of a negative UA.

Disclosures

The authors have nothing to disclose.

Reduction of antibiotic overuse is an important goal for improving the quality of care for children and is highlighted in many of the Choosing Wisely^® recommendations across disciplines.^1-3 However, the evidence supporting these recommendations vary widely and many are derived from expert opinion and clinical practice guidelines rather than from original research studies.² In this issue of the Journal of Hospital Medicine, Schroeder and colleagues identify a potential area of antibiotic overuse among young febrile infants with possible urinary tract infection (UTI).⁴ A wide variation in antibiotic treatment rates (0%-35%) was observed across 124 hospitals in the United States for febrile infants 7-60 days of age with uropathogen detection by urine culture but a negative urinalysis (UA). Treated infants with a negative UA were more likely to be younger (7-30 days), have respiratory symptoms, and were less likely to have abnormal inflammatory markers than infants with a positive UA.

Clinicians faced with the decision of whether or not to treat a febrile infant with uropathogen detection in the setting of a negative UA must weigh the potential benefits and harms of antibiotic use in this population. Withholding antibiotics for a young infant with UTI may increase the risk of recurrent UTI and renal scarring,^5,6 while antibiotic treatment in young infants can lead to the disruption of the gut microbiome, resulting in long-term consequences that are only beginning to be understood.^7-10

The American Academy of Pediatrics (AAP) UTI practice parameter requires a positive UA to establish the diagnosis of UTI in children 2-24 months of age.¹¹ This recommendation is based primarily on studies demonstrating that uropathogen detection in the setting of a negative UA commonly represents asymptomatic bacteriuria or contamination rather than true infection.^12-14 This is supported by research showing that the UA demonstrates near perfect (>99%) sensitivity for UTI in children with bacteremic UTI,^12,15 and studies demonstrating lower rates of subsequent urinary infections and renal injury among infants with uropathogen detection and a negative UA compared with those with uropathogen detection and a positive UA.^13,14,16

An important question is whether febrile infants within the first two months of life with uropathogen detection should be treated with antimicrobials regardless of UA findings or specifically in the setting of a negative UA. The AAP practice guideline¹¹ deliberately omits these young infants, recognizing that evidence derived from studies of older infants and children may not be applicable to this young age group, as they may not mount as robust an inflammatory response and thus may not demonstrate pyuria in the setting of a bacterial urinary infection. Schroeder et al. demonstrate lower rates of abnormal inflammatory markers in UA negative compared with UA positive infants, a finding the authors argue supports the possibility of asymptomatic bacteriuria or contamination rather than true infection.⁴ The counterargument is that young infants may not mount a significant inflammatory response to true infections.

The authors appropriately highlight the paucity of literature to help differentiate true infection from asymptomatic bacteriuria or contamination in infants less than two months of age. As infants in this age group are usually treated with antibiotics for a positive urine culture regardless of UA result, robust data on short- and long-term outcomes of untreated infants are lacking. Much of the existing literature evaluates the test performance of the UA for UTI using the urine culture as the reference standard, which presents inherent limitations with incorporating the results of the UA into the definition of UTI using these data. Additionally, reported test performance of the UA for UTI varies by uropathogen type,¹⁷ fever duration,¹⁸ associated bacteremia,¹⁹ and urine concentration,²⁰ which are important considerations when applying a strict definition of UTI that includes the UA in this age group. Conversely, more recent studies have demonstrated improved sensitivity of the dipstick and microscopic UA for the detection of UTI.^15,20,21 The improved test performance may not only enhance the use of the UA as a screen for UTI in this high-risk population but also allow its potential inclusion into the definition of UTI as the authors suggest, as previous false-negative UTIs would be less frequent with improved UA testing modalities.

Ultimately, what’s missing from the equation is whether treatment of young febrile infants with uropathogen detection in the setting of a negative UA affects either short-term or long-term complications of UTI. Unfortunately, limited information exists to help inform the decision to initiate antibiotic treatment for these infants. Ideally, this question can only be answered by either an observational study evaluating outcomes of untreated infants or a randomized trial of antibiotics for infants less than two months of age with uropathogen detection in the setting of a negative UA. Until then, we may continue to observe a wide variation in antibiotic treatment rates for febrile young infants with uropathogen detection in the setting of a negative UA.

Disclosures

The authors have nothing to disclose.

Every reader of a certain age will recognize this acronym: ADCVANDIML. In simpler times, we “admitted” to a location: medical intensive care unit, bone marrow transplant unit. At some point, admission orders changed from a synonym for “hospitalize” to chart evidence necessary for inpatient payment to the hospital. In the billing and payment world, “inpatient” and “outpatient” hospitalizations are paid at different rates. Observation stays are one type of “outpatient hospitalization,” a confusing and contradictory term to physicians and patients alike. In their article published in this month’s Journal of Hospital Medicine, Sheehy and colleagues attempt the herculean task of defining a reproducible methodology to identify observation hospital stays using Medicare claims data.¹ They highlight the complexity of claims data, the variability of revenue codes used, and the probable high frequency of status changes from outpatient observation to inpatient, and vice-versa, during a single hospitalization. They also argue for reform to simplify payment policy for hospitalized patients.

In October 2013, the Center for Medicare and Medicaid Services (CMS) changed the definition of “inpatient” in the Hospital Inpatient Prospective Payment System rule.² This change is known colloquially as the “two-midnight rule” and occurred on the heels of several years of Recovery Audit contractor (RAC) retroactive denials of short-stay inpatient payments to hospitals around the country. These denials appear to have been based solely on the visit status under which a claim was billed, rather than a dispute over the actual medical care delivered.³ The RAC audits alleged billions of dollars of improper payment to hospitals and resulted in a log-jam of hundreds of thousands of cases in the federal appeal system.⁴ The two-midnight rule altered the subjective characterization of an inpatient from patient-based (severity of illness) and physician-based (intensity of service) to an objective, time-based payment definition. For the hospital to submit a claim to Medicare Part A, a medical provider with admitting privileges should expect that the patient will need, for medically necessary reasons, a hospitalization that will span at least two midnights of hospital care. Notable exceptions to the rule include patients undergoing a procedure on the Medicare Inpatient Only list and hospitalizations that include an unplanned mechanical intubation. To receive payment for observation (an outpatient service billed under Part B) the physician must place an observation order in addition to other requirements. At its core, the two-midnight rule is a payment rule, not a patient care rule.

This change in the criteria for an inpatient hospitalization from a subjective to a more objective and measurable time-based criterion might lead us to believe that the process for determining the correct visit status would now be simple. Unfortunately, we are dealing with a messy real-world scenario, where doctors can make different judgments and patients can have an unpredictable hospital course. Physicians are familiar with the issues surrounding the choice of the “correct” admission order. In many hospitals, the Medicare patients in “observation” and those with an “inpatient” order can be on the same floor and even share the same room. From a hospital resource, nurse’s, and physician’s standpoint, the patients are often indistinguishable. While some facilities have observation units often associated with their emergency departments, the elderly and those patients with certain comorbidities can be excluded from these units based on protocols designed to improve outcomes and patient safety.

Additionally, most patients who spend at least one night in the hospital for medical treatment would not think that they could be an “outpatient.” To address this, CMS has produced specific beneficiary information⁵ and now requires hospitals to provide patients with the Medicare outpatient observation notice (MOON) if patients spend more than 24 hours in observation status.⁶ Beneficiaries must sign this notice, but unlike those admitted as inpatients, Medicare observation patients have no appeal rights. Recent articles in the lay press highlight the interplay between observation status, out-of-pocket expenses, and impact on postacute care.^7,8

Following the implementation of the two-midnight rule, CMS directed the regional Medicare Administrative Contractors to perform audits in every hospital in the country. This has led to system-based processes at most facilities directing the “proper” visit class orders for our patients: direct education to providers, electronic medical record fixes and hard-stops, and real-time communications from the utilization review nurses and staff. These processes, based on a payment rule are burdensome to patients, physicians, and hospital support staff.

It’s not surprising to see that the billing of hospital-based observation care is also a quagmire. The methods and results sections of Sheehy et al.’s article reads like a calculus textbook written in a foreign language on first pass, even to an expert. Adding to an already complex issue, since October 2013, a hospital’s Utilization Review physicians can also “self-deny” Medicare inpatient stays that do not meet the two-midnight rule payment criteria and still bill for most of Part B charges. These cases are sometimes referred to as “Part A to B rebills” and may or may not have been captured in the claims data reported by CMS and reviewed by Sheehy et al. These cases represent another important status change that should be tracked.

There is a multitude of opinions on the pros and cons of observation care as a payment policy, and the data presented by Sheehy et al. is further evidence that the line between inpatient and observation hospitalizations remains blurred and mutable. The authors demonstrate the need for a consistent methodology to define observation stays and ultimately to study them using claims-based data. Simplicity may be the answer, but first, we must know what we are doing, then we can have a debate on whether or not it needs to change.

Disclosures

The authors have nothing to disclose.

Every reader of a certain age will recognize this acronym: ADCVANDIML. In simpler times, we “admitted” to a location: medical intensive care unit, bone marrow transplant unit. At some point, admission orders changed from a synonym for “hospitalize” to chart evidence necessary for inpatient payment to the hospital. In the billing and payment world, “inpatient” and “outpatient” hospitalizations are paid at different rates. Observation stays are one type of “outpatient hospitalization,” a confusing and contradictory term to physicians and patients alike. In their article published in this month’s Journal of Hospital Medicine, Sheehy and colleagues attempt the herculean task of defining a reproducible methodology to identify observation hospital stays using Medicare claims data.¹ They highlight the complexity of claims data, the variability of revenue codes used, and the probable high frequency of status changes from outpatient observation to inpatient, and vice-versa, during a single hospitalization. They also argue for reform to simplify payment policy for hospitalized patients.

In October 2013, the Center for Medicare and Medicaid Services (CMS) changed the definition of “inpatient” in the Hospital Inpatient Prospective Payment System rule.² This change is known colloquially as the “two-midnight rule” and occurred on the heels of several years of Recovery Audit contractor (RAC) retroactive denials of short-stay inpatient payments to hospitals around the country. These denials appear to have been based solely on the visit status under which a claim was billed, rather than a dispute over the actual medical care delivered.³ The RAC audits alleged billions of dollars of improper payment to hospitals and resulted in a log-jam of hundreds of thousands of cases in the federal appeal system.⁴ The two-midnight rule altered the subjective characterization of an inpatient from patient-based (severity of illness) and physician-based (intensity of service) to an objective, time-based payment definition. For the hospital to submit a claim to Medicare Part A, a medical provider with admitting privileges should expect that the patient will need, for medically necessary reasons, a hospitalization that will span at least two midnights of hospital care. Notable exceptions to the rule include patients undergoing a procedure on the Medicare Inpatient Only list and hospitalizations that include an unplanned mechanical intubation. To receive payment for observation (an outpatient service billed under Part B) the physician must place an observation order in addition to other requirements. At its core, the two-midnight rule is a payment rule, not a patient care rule.

This change in the criteria for an inpatient hospitalization from a subjective to a more objective and measurable time-based criterion might lead us to believe that the process for determining the correct visit status would now be simple. Unfortunately, we are dealing with a messy real-world scenario, where doctors can make different judgments and patients can have an unpredictable hospital course. Physicians are familiar with the issues surrounding the choice of the “correct” admission order. In many hospitals, the Medicare patients in “observation” and those with an “inpatient” order can be on the same floor and even share the same room. From a hospital resource, nurse’s, and physician’s standpoint, the patients are often indistinguishable. While some facilities have observation units often associated with their emergency departments, the elderly and those patients with certain comorbidities can be excluded from these units based on protocols designed to improve outcomes and patient safety.

Additionally, most patients who spend at least one night in the hospital for medical treatment would not think that they could be an “outpatient.” To address this, CMS has produced specific beneficiary information⁵ and now requires hospitals to provide patients with the Medicare outpatient observation notice (MOON) if patients spend more than 24 hours in observation status.⁶ Beneficiaries must sign this notice, but unlike those admitted as inpatients, Medicare observation patients have no appeal rights. Recent articles in the lay press highlight the interplay between observation status, out-of-pocket expenses, and impact on postacute care.^7,8

Following the implementation of the two-midnight rule, CMS directed the regional Medicare Administrative Contractors to perform audits in every hospital in the country. This has led to system-based processes at most facilities directing the “proper” visit class orders for our patients: direct education to providers, electronic medical record fixes and hard-stops, and real-time communications from the utilization review nurses and staff. These processes, based on a payment rule are burdensome to patients, physicians, and hospital support staff.

It’s not surprising to see that the billing of hospital-based observation care is also a quagmire. The methods and results sections of Sheehy et al.’s article reads like a calculus textbook written in a foreign language on first pass, even to an expert. Adding to an already complex issue, since October 2013, a hospital’s Utilization Review physicians can also “self-deny” Medicare inpatient stays that do not meet the two-midnight rule payment criteria and still bill for most of Part B charges. These cases are sometimes referred to as “Part A to B rebills” and may or may not have been captured in the claims data reported by CMS and reviewed by Sheehy et al. These cases represent another important status change that should be tracked.

There is a multitude of opinions on the pros and cons of observation care as a payment policy, and the data presented by Sheehy et al. is further evidence that the line between inpatient and observation hospitalizations remains blurred and mutable. The authors demonstrate the need for a consistent methodology to define observation stays and ultimately to study them using claims-based data. Simplicity may be the answer, but first, we must know what we are doing, then we can have a debate on whether or not it needs to change.

Disclosures

The authors have nothing to disclose.

Every reader of a certain age will recognize this acronym: ADCVANDIML. In simpler times, we “admitted” to a location: medical intensive care unit, bone marrow transplant unit. At some point, admission orders changed from a synonym for “hospitalize” to chart evidence necessary for inpatient payment to the hospital. In the billing and payment world, “inpatient” and “outpatient” hospitalizations are paid at different rates. Observation stays are one type of “outpatient hospitalization,” a confusing and contradictory term to physicians and patients alike. In their article published in this month’s Journal of Hospital Medicine, Sheehy and colleagues attempt the herculean task of defining a reproducible methodology to identify observation hospital stays using Medicare claims data.¹ They highlight the complexity of claims data, the variability of revenue codes used, and the probable high frequency of status changes from outpatient observation to inpatient, and vice-versa, during a single hospitalization. They also argue for reform to simplify payment policy for hospitalized patients.

In October 2013, the Center for Medicare and Medicaid Services (CMS) changed the definition of “inpatient” in the Hospital Inpatient Prospective Payment System rule.² This change is known colloquially as the “two-midnight rule” and occurred on the heels of several years of Recovery Audit contractor (RAC) retroactive denials of short-stay inpatient payments to hospitals around the country. These denials appear to have been based solely on the visit status under which a claim was billed, rather than a dispute over the actual medical care delivered.³ The RAC audits alleged billions of dollars of improper payment to hospitals and resulted in a log-jam of hundreds of thousands of cases in the federal appeal system.⁴ The two-midnight rule altered the subjective characterization of an inpatient from patient-based (severity of illness) and physician-based (intensity of service) to an objective, time-based payment definition. For the hospital to submit a claim to Medicare Part A, a medical provider with admitting privileges should expect that the patient will need, for medically necessary reasons, a hospitalization that will span at least two midnights of hospital care. Notable exceptions to the rule include patients undergoing a procedure on the Medicare Inpatient Only list and hospitalizations that include an unplanned mechanical intubation. To receive payment for observation (an outpatient service billed under Part B) the physician must place an observation order in addition to other requirements. At its core, the two-midnight rule is a payment rule, not a patient care rule.

This change in the criteria for an inpatient hospitalization from a subjective to a more objective and measurable time-based criterion might lead us to believe that the process for determining the correct visit status would now be simple. Unfortunately, we are dealing with a messy real-world scenario, where doctors can make different judgments and patients can have an unpredictable hospital course. Physicians are familiar with the issues surrounding the choice of the “correct” admission order. In many hospitals, the Medicare patients in “observation” and those with an “inpatient” order can be on the same floor and even share the same room. From a hospital resource, nurse’s, and physician’s standpoint, the patients are often indistinguishable. While some facilities have observation units often associated with their emergency departments, the elderly and those patients with certain comorbidities can be excluded from these units based on protocols designed to improve outcomes and patient safety.

Additionally, most patients who spend at least one night in the hospital for medical treatment would not think that they could be an “outpatient.” To address this, CMS has produced specific beneficiary information⁵ and now requires hospitals to provide patients with the Medicare outpatient observation notice (MOON) if patients spend more than 24 hours in observation status.⁶ Beneficiaries must sign this notice, but unlike those admitted as inpatients, Medicare observation patients have no appeal rights. Recent articles in the lay press highlight the interplay between observation status, out-of-pocket expenses, and impact on postacute care.^7,8

Following the implementation of the two-midnight rule, CMS directed the regional Medicare Administrative Contractors to perform audits in every hospital in the country. This has led to system-based processes at most facilities directing the “proper” visit class orders for our patients: direct education to providers, electronic medical record fixes and hard-stops, and real-time communications from the utilization review nurses and staff. These processes, based on a payment rule are burdensome to patients, physicians, and hospital support staff.

It’s not surprising to see that the billing of hospital-based observation care is also a quagmire. The methods and results sections of Sheehy et al.’s article reads like a calculus textbook written in a foreign language on first pass, even to an expert. Adding to an already complex issue, since October 2013, a hospital’s Utilization Review physicians can also “self-deny” Medicare inpatient stays that do not meet the two-midnight rule payment criteria and still bill for most of Part B charges. These cases are sometimes referred to as “Part A to B rebills” and may or may not have been captured in the claims data reported by CMS and reviewed by Sheehy et al. These cases represent another important status change that should be tracked.

There is a multitude of opinions on the pros and cons of observation care as a payment policy, and the data presented by Sheehy et al. is further evidence that the line between inpatient and observation hospitalizations remains blurred and mutable. The authors demonstrate the need for a consistent methodology to define observation stays and ultimately to study them using claims-based data. Simplicity may be the answer, but first, we must know what we are doing, then we can have a debate on whether or not it needs to change.

Disclosures

The authors have nothing to disclose.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.