Samir S. Shah, MD, MSCE

Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Pediatric hospitalists should be able to:

• Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
• Explain the pathophysiology of bone and joint infections.
• Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
• Explain risk factors for bone and joint infections, including sickle cell disease.
• Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
• Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
• State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
• Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
• Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
• Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
• Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
• Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
• Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
• Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Pediatric hospitalists should be able to:

• Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
• Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
• Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
• Manage pain effectively for children with bone and joint infections.
• Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
• Access and arrange for pediatric home care services as appropriate.
• Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Pediatric hospitalists should be able to:

• Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
• Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

• Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
• Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
• Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
• Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.

Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Pediatric hospitalists should be able to:

• Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
• Explain the pathophysiology of bone and joint infections.
• Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
• Explain risk factors for bone and joint infections, including sickle cell disease.
• Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
• Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
• State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
• Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
• Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
• Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
• Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
• Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
• Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
• Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Pediatric hospitalists should be able to:

• Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
• Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
• Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
• Manage pain effectively for children with bone and joint infections.
• Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
• Access and arrange for pediatric home care services as appropriate.
• Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Pediatric hospitalists should be able to:

• Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
• Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

• Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
• Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
• Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
• Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.

Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Pediatric hospitalists should be able to:

• Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
• Explain the pathophysiology of bone and joint infections.
• Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
• Explain risk factors for bone and joint infections, including sickle cell disease.
• Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
• Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
• State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
• Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
• Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
• Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
• Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
• Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
• Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
• Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Pediatric hospitalists should be able to:

• Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
• Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
• Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
• Manage pain effectively for children with bone and joint infections.
• Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
• Access and arrange for pediatric home care services as appropriate.
• Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Pediatric hospitalists should be able to:

• Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
• Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

• Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
• Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
• Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
• Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

• 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
• 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

• 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
• 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

• 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
• 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.¹ The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.² Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.^3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.^5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.^6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)^8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.^13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,^3,15,16 there remains much work to be done.^1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study¹⁷ by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.¹⁷ This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.¹⁸

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.¹⁹ On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,^17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.^21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.^23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).²⁶ This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,¹⁷ developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation²⁷ was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.²⁷ Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.²⁸ As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.^27,28 Coded data were maintained in an electronic database accessible only to study personnel.

Participants

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.² During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.^19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”^29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.^31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.³⁵ Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.³⁶ found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.²⁹ Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,^8-10,12 but few have looked at it from the perspective of the caregiver.¹³ After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.^37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.¹ The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.² Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.^3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.^5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.^6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)^8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.^13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,^3,15,16 there remains much work to be done.^1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study¹⁷ by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.¹⁷ This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.¹⁸

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.¹⁹ On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,^17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.^21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.^23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).²⁶ This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,¹⁷ developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation²⁷ was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.²⁷ Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.²⁸ As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.^27,28 Coded data were maintained in an electronic database accessible only to study personnel.

Participants

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.² During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.^19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”^29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.^31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.³⁵ Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.³⁶ found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.²⁹ Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,^8-10,12 but few have looked at it from the perspective of the caregiver.¹³ After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.^37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.¹ The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.² Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.^3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.^5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.^6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)^8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.^13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,^3,15,16 there remains much work to be done.^1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study¹⁷ by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.¹⁷ This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.¹⁸

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.¹⁹ On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,^17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.^21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.^23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).²⁶ This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,¹⁷ developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation²⁷ was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.²⁷ Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.²⁸ As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.^27,28 Coded data were maintained in an electronic database accessible only to study personnel.

Participants

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.² During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.^19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”^29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.^31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.³⁵ Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.³⁶ found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.²⁹ Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,^8-10,12 but few have looked at it from the perspective of the caregiver.¹³ After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.^37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,¹ with 8% of pediatric residency graduates each year entering the field.² Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.^3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.^7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.⁹

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.^11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.^10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.^11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.²¹ Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule²² and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),¹¹ the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).^19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.²⁰ Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.^15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.²³ A lack of time and fear of rejection after peer review are commonly cited explanations.^18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.¹⁸ To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.¹⁶ Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.²⁰ Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,¹² with publication rates as high as 98% within 3 years of presentation.¹⁷ In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,¹² authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.¹⁶ Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,¹ with 8% of pediatric residency graduates each year entering the field.² Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.^3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.^7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.⁹

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.^11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.^10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.^11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.²¹ Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule²² and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),¹¹ the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).^19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.²⁰ Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.^15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.²³ A lack of time and fear of rejection after peer review are commonly cited explanations.^18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.¹⁸ To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.¹⁶ Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.²⁰ Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,¹² with publication rates as high as 98% within 3 years of presentation.¹⁷ In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,¹² authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.¹⁶ Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,¹ with 8% of pediatric residency graduates each year entering the field.² Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.^3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.^7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.⁹

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.^11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.^10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.^11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.²¹ Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule²² and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),¹¹ the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).^19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.²⁰ Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.^15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.²³ A lack of time and fear of rejection after peer review are commonly cited explanations.^18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.¹⁸ To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.¹⁶ Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.²⁰ Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,¹² with publication rates as high as 98% within 3 years of presentation.¹⁷ In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,¹² authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.¹⁶ Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose