Observation Encounters and Length of Stay Benchmarking in Children's Hospitals.

BACKGROUND AND OBJECTIVES: Length of stay (LOS) is a common benchmarking measure for hospital resource use and quality. Observation status (OBS) is considered an outpatient service despite the use of the same facilities as inpatient status (IP) in most children's hospitals, and LOS calculations often exclude OBS stays. Variability in the use of OBS by hospitals may significantly impact calculated LOS. We sought to determine the impact of including OBS in calculating LOS across children's hospitals. METHODS: Retrospective cohort study of hospitalized children (age <19 years) in 2017 from the Pediatric Health Information System (Children's Hospital Association, Lenexa, KS). Normal newborns, transfers, deaths, and hospitals not reporting LOS in hours were excluded. Risk-adjusted geometric mean length of stay (RA-LOS) for IP-only and IP plus OBS was calculated and each hospital was ranked by quintile. RESULTS: In 2017, 45 hospitals and 625 032 hospitalizations met inclusion criteria (IP = 410 731 [65.7%], OBS = 214 301 [34.3%]). Across hospitals, OBS represented 0.0% to 60.3% of total discharges. The RA-LOS (SD) in hours for IP and IP plus OBS was 75.2 (2.6) and 54.3 (2.7), respectively (P < .001). For hospitals reporting OBS, the addition of OBS to IP RA-LOS calculations resulted in a decrease in RA-LOS compared with IP encounters alone. Three-fourths of hospitals changed ≥1 quintile in LOS ranking with the inclusion of OBS. CONCLUSIONS: Children's hospitals exhibit significant variability in the assignment of OBS to hospitalized patients and inclusion of OBS significantly impacts RA-LOS calculations. Careful consideration should be given to the inclusion of OBS when determining RA-LOS for benchmarking, quality and resource use measurements.

Room Costs for Common Pediatric Hospitalizations and Cost-Reducing Quality Initiatives.

BACKGROUND: Improvement initiatives promote safe and efficient care for hospitalized children. However, these may be associated with limited cost savings. In this article, we sought to understand the potential financial benefit yielded by improvement initiatives by describing the inpatient allocation of costs for common pediatric diagnoses. METHODS: This study is a retrospective cross-sectional analysis of pediatric patients aged 0 to 21 years from 48 children's hospitals included in the Pediatric Health Information System database from January 1, 2017, to December 31, 2017. We included hospitalizations for 8 common inpatient pediatric diagnoses (seizure, bronchiolitis, asthma, pneumonia, acute gastroenteritis, upper respiratory tract infection, other gastrointestinal diagnoses, and skin and soft tissue infection) and categorized the distribution of hospitalization costs (room, clinical, laboratory, imaging, pharmacy, supplies, and other). We summarized our findings with mean percentages and percent of total costs and used mixed-effects models to account for disease severity and to describe hospital-level variation. RESULTS: For 195 436 hospitalizations, room costs accounted for 52.5% to 70.3% of total hospitalization costs. We observed wide hospital-level variation in nonroom costs for the same diagnoses (25%-81% for seizure, 12%-51% for bronchiolitis, 19%-63% for asthma, 19%-62% for pneumonia, 21%-78% for acute gastroenteritis, 21%-63% for upper respiratory tract infection, 28%-69% for other gastrointestinal diagnoses, and 21%-71% for skin and soft tissue infection). However, to achieve a cost reduction equal to 10% of room costs, large, often unattainable reductions (>100%) in nonroom cost categories are needed. CONCLUSIONS: Inconsistencies in nonroom costs for similar diagnoses suggest hospital-level treatment variation and improvement opportunities. However, individual improvement initiatives may not result in significant cost savings without specifically addressing room costs.

Repeat Laboratory Testing in the Pediatric Emergency Department: How Often and How Important?

BACKGROUND: Little is known about repeat testing for patients admitted to children's hospitals from the emergency department (ED). OBJECTIVE: The objective of this study was to describe the trend of repeat laboratory testing from a children's hospital ED. METHODS: Laboratory studies were analyzed for July 2002 to June 2010 for complete blood counts (CBCs; 7 years), basic metabolic panels (BMPs; 2.5 years), and coagulation studies (7 years) ordered and reordered in the ED within 8 hours for patients admitted to the hospital. Results for tests were generated and classified into high, low, and normal based on reference ranges. To reflect actual practice, we expanded the normal range from 95% of lower bound to 105% of upper bound. RESULTS: A total of 37,035 CBCs, 11,414 BMPs, and 3903 coagulation studies were ordered. Proportions of these tests repeated were 0.9%, 1.9%, and 1.9%, respectively. Mean time to repeat was 2 hours. For CBCs, 25% of repeats were for a missing component; 35% were for low platelet counts. Sixty-eight percent of initial BMPs were repeated for high potassium. Half of coagulation studies were repeated for high prothrombin time; 36% were repeated for a missing component. On repeat, 75% of BMPs with high potassium levels and 65% of CBCs with low platelet count returned normal values, but 16% of coagulation studies repeated for high prothrombin time returned normal values. CONCLUSIONS: Repeat ED laboratory testing occurs infrequently at a children's hospital, and a large proportion of repeats is attributed to missing results. When repeated, abnormal results on initial studies are often returned as normal.

Using Length of Stay to Understand Patient Flow for Pediatric Inpatients.

OBJECTIVES: Develop and test a new metric to assess meaningful variability in inpatient flow. METHODS: Using the pediatric administrative dataset, Pediatric Health Information System, that quantifies the length of stay (LOS) in hours, all inpatient and observation encounters with 21 common diagnoses were included from the calendar year 2013 in 38 pediatric hospitals. Two mutually exclusive composite groups based on diagnosis and presence or absence of an ICU hospitalization termed Acute Care Composite (ACC) and ICU Composite (ICUC), respectively, were created. These composites consisted of an observed-to-expected (O/E) LOS as well as an excess LOS percentage (ie, the percent of day beyond expected). Seven-day all-cause risk-adjusted rehospitalizations was used as a balancing measure. The combination of the ACC, the ICUC, and the rehospitalization measures forms this new metric. RESULTS: The diagnosis groups in the ACC and the ICUC included 113,768 and 38,400 hospitalizations, respectively. The ACC had a median O/E LOS of 1.0, a median excess LOS percentage of 23.9% and a rehospitalization rate of 1.7%. The ICUC had a median O/E LOS of 1.1, a median excess LOS percentage of 32.3%, and rehospitalization rate of 4.9%. There was no relationship of O/E LOS and rehospitalization for either ACC or ICUC. CONCLUSIONS: This metric shows variation among hospitals and could allow a pediatric hospital to assess the performance of inpatient flow.

Hypothetical Network Adequacy Schemes For Children Fail To Ensure Patients' Access To In-Network Children's Hospital.

Insurers are increasingly adopting narrow network strategies. Little is known about how these strategies may affect children's access to needed specialty care. We examined the percentage of pediatric specialty hospitalizations that would be beyond existing Medicare Advantage network adequacy distance requirements for adult hospital care and, as a secondary analysis, a pediatric adaptation of the Medicare Advantage requirements. We examined 748,920 hospitalizations at eighty-one children's hospitals that submitted data for the period October 2014-September 2015. Nearly half of specialty hospitalizations were outside the Medicare Advantage distance requirements. Under the pediatric adaptation, there was great variability among the hospitals, with the percent of hospitalizations beyond the distance requirements ranging from less than 1 percent to 35 percent. Instead of, or in addition to, time and distance standards, policy makers may need to consider more nuanced network definitions, including functional capabilities of the pediatric care network or clear exception policies for essential specialty care services.

Disparities in Outcomes and Resource Use After Hospitalization for Cardiac Surgery by Neighborhood Income.

BACKGROUND: Significant disparities exist between patients of different races and with different family incomes; less is understood regarding community-level factors on outcomes. METHODS: In this study, we used linked data from the Pediatric Health Information System database and the US Census Bureau to examine associations between median annual household income by zip code and mortality, length of stay, inpatient standardized costs, and costs per day, over and above the effects of race and payer, first for children undergoing cardiac surgery (2005-2015) and then for all pediatric discharges (2012-2015). Median community-level income was examined as continuous and categorical (by quartile) predictors. Hierarchical logistic and censored linear regression models were constructed. To these models, patient and surgical characteristics, year, race, payer, state, urban or rural designation, and center fixed effects were added. RESULTS: We identified 101 013 cardiac surgical (and 857 833 total) hospitalizations from 46 institutions. Children from the lowest-income neighborhoods who were undergoing cardiac surgery had 1.18 times the odds of mortality (95% confidence interval [CI]: 1.03 to 1.35), 7% longer lengths of stay (CI: 1% to 14%), and 7% higher standardized costs (CI: 1% to 14%) than children from the highest-income neighborhoods. Results for all children were similar, both with and without any major chronic conditions. The effects of neighborhood were only partially explained by differences in race, payer, or the centers at which patients received care. There were no differences in costs per day. CONCLUSIONS: Children from lower-income neighborhoods are at increased risk of mortality and use more resource intensive care than children from higher-income communities, even after accounting for disparities between races, payers, and centers.

Regional Variation in Standardized Costs of Care at Children's Hospitals.

OBJECTIVE: (1) To evaluate regional variation in costs of care for 3 inpatient pediatric conditions, (2) assess potential drivers of variation, and (3) estimate cost savings from reducing variation. DESIGN/SETTING: Retrospective cohort study of hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 children

Adding Social Determinant Data Changes Children's Hospitals' Readmissions Performance.

OBJECTIVES: To determine whether social determinants of health (SDH) risk adjustment changes hospital-level performance on the 30-day Pediatric All-Condition Readmission (PACR) measure and improves fit and accuracy of discharge-level models. STUDY DESIGN: We performed a retrospective cohort study of all hospital discharges meeting criteria for the PACR from 47 hospitals in the Pediatric Health Information database from January to December 2014. We built four nested regression models by sequentially adding risk adjustment factors as follows: chronic condition indicators (CCIs); PACR patient factors (age and sex); electronic health record-derived SDH (race, ethnicity, payer), and zip code-linked SDH (families below poverty level, vacant housing units, adults without a high school diploma, single-parent households, median household income, unemployment rate). For each model, we measured the change in hospitals' readmission decile-rank and assessed model fit and accuracy. RESULTS: For the 458 686 discharges meeting PACR inclusion criteria, in multivariable models, factors associated with higher discharge-level PACR measure included age <1 year, female sex, 1 of 17 CCIs, higher CCI count, Medicaid insurance, higher median household income, and higher percentage of single-parent households. Adjustment for SDH made small but significant improvements in fit and accuracy of discharge-level PACR models, with larger effect at the hospital level, changing decile-rank for 17 of 47 hospitals. CONCLUSIONS: We found that risk adjustment for SDH changed hospitals' readmissions rate rank order. Hospital-level changes in relative readmissions performance can have considerable financial implications; thus, for pay for performance measures calculated at the hospital level, and for research associated therewith, our findings support the inclusion of SDH variables in risk adjustment.

Financial Analysis of an Intensive Pediatric Continuous Positive Airway Pressure Program.

Study Objectives: Continuous positive airway pressure (CPAP) is effective in treating obstructive sleep apnea in children, but adherence to therapy is low. Our center created an intensive program that aimed to improve adherence. Our objective was to estimate the program's efficacy, cost, revenue and break-even point in a generalizable manner relative to a standard approach. Methods: The intensive program included device consignment, behavioral psychology counseling, and follow-up telephone calls. Economic modeling considered the costs, revenue and break-even point. Costs were derived from national salary reports and the Pediatric Health Information System. The 2015 Medicare reimbursement schedule provided revenue estimates. Results: Prior to the intensive CPAP program, only 67.6% of 244 patients initially prescribed CPAP appeared for follow-up visits and only 38.1% had titration polysomnograms. In contrast, 81.4% of 275 patients in the intensive program appeared for follow-up visits (p < .001) and 83.6% had titration polysomnograms (p < .001). Medicare reimbursement levels would be insufficient to cover the estimated costs of the intensive program; break-even points would need to be 1.29-2.08 times higher to cover the costs. Conclusions: An intensive CPAP program leads to substantially higher follow-up and CPAP titration rates, but costs are higher. While affordable at our institution due to the local payer mix and revenue, Medicare reimbursement levels would not cover estimated costs. This study highlights the need for enhanced funding for pediatric CPAP programs, due to the special needs of this population and the long-term health risks of suboptimally treated obstructive sleep apnea.

Children's Hospital Characteristics and Readmission Metrics.

BACKGROUND AND OBJECTIVE: Like their adult counterparts, pediatric hospitals are increasingly at risk for financial penalties based on readmissions. Limited information is available on how the composition of a hospital's patient population affects performance on this metric and hence affects reimbursement for hospitals providing pediatric care. We sought to determine whether applying different readmission metrics differentially affects hospital performance based on the characteristics of patients a hospital serves. METHODS: We performed a cross-sectional analysis of 64 children's hospitals from the Children's Hospital Association Case Mix Comparative Database 2012 and 2013. We calculated 30-day observed-to-expected readmission ratios by using both all-cause (AC) and Potentially Preventable Readmissions (PPR) metrics. We examined the association between observed-to-expected rates and hospital characteristics by using multivariable linear regression. RESULTS: We examined a total of 1 416 716 hospitalizations. The mean AC 30-day readmission rate was 11.3% (range 4.3%-19.6%); the mean PPR rate was 4.9% (range 2.9%-6.9%). The average 30-day AC observed-to-expected ratio was 0.96 (range 0.63-1.23), compared with 0.95 (range 0.65-1.23) for PPR; 59% of hospitals performed better than expected on both measures. Hospitals with higher volumes, lower percentages of infants, and higher percentage of patients with low income performed worse than expected on PPR. CONCLUSIONS: High-volume hospitals, those that serve fewer infants, and those with a high percentage of patients from low-income neighborhoods have higher than expected PPR rates and are at higher risk of reimbursement penalties.

Financial Loss for Inpatient Care of Medicaid-Insured Children.

Importance: Medicaid payments tend to be less than the cost of care. Federal Disproportionate Share Hospital (DSH) payments help hospitals recover such uncompensated costs of Medicaid-insured and uninsured patients. The Patient Protection and Affordable Care Act reduces DSH payments in anticipation of fewer uninsured patients and therefore decreased uncompensated care. However, unlike adults, few hospitalized children are uninsured, while many have Medicaid coverage. Therefore, DSH payment reductions may expose extensive Medicaid financial losses for hospitals serving large absolute numbers of children. Objectives: To identify types of hospitals with the highest Medicaid losses from pediatric inpatient care and to estimate the proportion of losses recovered through DSH payments. Design, Setting, and Participants: This retrospective cross-sectional analysis evaluated Medicaid-insured hospital discharges of patients 20 years and younger from 23 states in the 2009 Kids' Inpatient Database. The dates of the analysis were March to September 2015. Hospitals were categorized as freestanding children's hospitals (FSCHs), children's hospitals within general hospitals, non-children's hospital teaching hospitals, and non-children's hospital nonteaching hospitals. Financial records of FSCHs in the data set were used to estimate the proportion of Medicaid losses recovered through DSH payments. Main Outcomes and Measures: Hospital financial losses from inpatient care of Medicaid-insured children (defined as the reimbursement minus the cost of care) were compared across hospital types. For our subsample of FSCHs, Medicaid-insured inpatient financial losses were calculated with and without each hospital's DSH payment. Results: The 2009 Kids' Inpatient Database study population included 1485 hospitals and 843 725 Medicaid-insured discharges. Freestanding children's hospitals had a higher median number of Medicaid-insured discharges (4082; interquartile range [IQR], 3524-5213) vs non-children's hospital teaching hospitals (674; IQR, 258-1414) and non-children's hospital nonteaching hospitals (161; IQR, 41-420). Freestanding children's hospitals had the largest median Medicaid losses from pediatric inpatient care (-$9 722 367; IQR, -$16 248 369 to -$2 137 902). Smaller losses were experienced by non-children's hospital teaching hospitals (-$204 100; IQR, -$1 014 100 to $14 700]) and non-children's hospital nonteaching hospitals (-$28 310; IQR, -$152 370 to $9040]). Disproportionate Share Hospital payments to FSCHs reduced their Medicaid losses by almost half. Conclusions and Relevance: Estimated financial losses from pediatric inpatients covered by Medicaid were much larger for FSCHs than for other hospital types. For children's hospitals, small anticipated increases in insured children are unlikely to offset the reductions in DSH payments.

Developing the Capacity for Rapid-Cycle Improvement at a Large Freestanding Children's Hospital.

BACKGROUND: To develop the capacity for rapid-cycle improvement at the unit level, a large freestanding children's hospital designated 2 inpatient units with normal patient loads and workforce as pilot "Innovation Units" where frontline staff was trained to lead rigorous improvement portfolios. METHODS: Frontline staff received improvement training, and interdisciplinary teams brainstormed ideas for tests of change. Ideas were prioritized using an impact-effort evaluation and an assessment of how they aligned with high-level goals. A template for each test summarized the following: the opportunity for improvement, the test being conducted, dates for the tests, driver diagrams, metrics to measure effects, baseline data, results, findings, and next steps. Successful interventions were implemented and disseminated to other units. RESULTS: Multidisciplinary staff generated 150 improvement ideas and Innovation Units collectively ran >40 plan-do-study-act cycles. Of the 10 distinct improvement projects, elements of all 10 were deemed "successful" and fully implemented on the unit, and elements from 8 were spread to other units. More than 3 years later, elements of all of the successful improvements are still in practice in some form on the units, and each unit has tested >20 additional improvement ideas, using multiple plan-do-study-act cycles to refine them. CONCLUSIONS: The Innovation Unit model successfully engaged frontline staff in improvement work and established a sustainable system and framework for managing rigorous improvement portfolios at the unit level. Other hospitals and health care delivery settings may find our quality improvement approach helpful, especially because it is rooted in the microsystem of care delivery.

Association of Social Determinants With Children's Hospitals' Preventable Readmissions Performance.

IMPORTANCE: Performance-measure risk adjustment is of great interest to hospital stakeholders who face substantial financial penalties from readmissions pay-for-performance (P4P) measures. Despite evidence of the association between social determinants of health (SDH) and individual patient readmission risk, the effect of risk adjusting for SDH on readmissions P4P penalties to hospitals is not well understood. OBJECTIVE: To determine whether risk adjustment for commonly available SDH measures affects the readmissions-based P4P penalty status of a national cohort of children's hospitals. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 43 free-standing children's hospitals within the Pediatric Health Information System database in the calendar year 2013. We evaluated hospital discharges from 2013 that met criteria for 3M Health Information Systems' potentially preventable readmissions measure for calendar year 2013. The analysis was conducted from July 2015 to August 2015. EXPOSURES: Two risk-adjustment models: a baseline model adjusted for severity of illness and an SDH-enhanced model that adjusted for severity of illness and the following 4 SDH variables: race, ethnicity, payer, and median household income for the patient's home zip code. MAIN OUTCOMES AND MEASURES: Change in a hospital's potentially preventable readmissions penalty status (ie, change in whether a hospital exceeded the penalty threshold) using an observed-to-expected potentially preventable readmissions ratio of 1.0 as a penalty threshold. RESULTS: For the 179,400 hospital discharges from the 43 hospitals meeting inclusion criteria, median (interquartile range [IQR]) hospital-level percentages for the SDH variables were 39.2% nonwhite (n = 71,300; IQR, 28.6%-54.6%), 17.9% Hispanic (n = 32,060; IQR, 6.7%-37.0%), and 58.7% publicly insured (n = 106,116; IQR, 50.4%-67.8%). The hospital median household income for the patient's home zip code was $ 40,674 (IQR, $ 35,912-$ 46,190). When compared with the baseline model, adjustment for SDH resulted in a change in penalty status for 3 hospitals within the 15-day window (2 were no longer above the penalty threshold and 1 was newly penalized) and 5 hospitals within the 30-day window (3 were no longer above the penalty threshold and 2 were newly penalized). CONCLUSIONS AND RELEVANCE: Risk adjustment for SDH changed hospitals' penalty status on a readmissions-based P4P measure. Without adjusting P4P measures for SDH, hospitals may receive penalties partially related to patient SDH factors beyond the quality of hospital care.

Determining preventability of pediatric readmissions using fault tree analysis.

BACKGROUND: Previous studies attempting to distinguish preventable from nonpreventable readmissions reported challenges in completing reviews efficiently and consistently. OBJECTIVES: (1) Examine the efficiency and reliability of a Web-based fault tree tool designed to guide physicians through chart reviews to a determination about preventability. (2) Investigate root causes of general pediatrics readmissions and identify the percent that are preventable. DESIGN/SETTING/PATIENTS: General pediatricians from The Children's Hospital of Philadelphia used a Web-based fault tree tool to classify root causes of all general pediatrics 15-day readmissions in 2014. INTERVENTION/MEASUREMENTS: The tool guided reviewers through a logical progression of questions, which resulted in 1 of 18 root causes of readmission, 8 of which were considered potentially preventable. Twenty percent of cases were cross-checked to measure inter-rater reliability. RESULTS: Of the 7252 discharges, 248 were readmitted, for an all-cause general pediatrics 15-day readmission rate of 3.4%. Of those readmissions, 15 (6.0%) were deemed potentially preventable, corresponding to 0.2% of total discharges. The most common cause of potentially preventable readmissions was premature discharge. For the 50 cross-checked cases, both reviews resulted in the same root cause for 44 (86%) of files (κ = 0.79; 95% confidence interval: 0.60-0.98). Completing 1 review using the tool took approximately 20 minutes. CONCLUSION: The Web-based fault tree tool helped physicians to identify root causes of hospital readmissions and classify them as either preventable or not preventable in an efficient and consistent way. It also confirmed that only a small percentage of general pediatrics 15-day readmissions are potentially preventable. Journal of Hospital Medicine 2016;11:329-335. © 2016 Society of Hospital Medicine.

Description and Evaluation of an Educational Intervention on Health Care Costs and Value.

OBJECTIVE: There is growing consensus that to ensure that health care dollars are spent efficiently, physicians need more training in how to provide high-value, cost-conscious care. Thus, in fiscal year 2014, The Children's Hospital of Philadelphia piloted a 9-part curriculum on health care costs and value for faculty in the Division of General Pediatrics. This study uses baseline and postintervention surveys to gauge knowledge, perceptions, and views on these issues and to assess the efficacy of the pilot curriculum. METHODS: Faculty completed surveys about their knowledge and perceptions about health care costs and value and their views on the role physicians should play in containing costs and promoting value. Baseline and postintervention responses were compared and analyzed on the basis of how many of the sessions respondents attended. RESULTS: Sixty-two faculty members completed the baseline survey (71% response rate), and 45 faculty members completed the postintervention survey (63% response rate). Reported knowledge of health care costs and value increased significantly in the postintervention survey (P=.04 and P<.001). Odds of being knowledgeable about costs and value were 2.42 (confidence interval: 1.05-5.58) and 6.22 times greater (confidence interval: 2.29-16.90), respectively, postintervention. Reported knowledge of health care costs and value increased with number of sessions attended (P=.01 and P<.001). CONCLUSIONS: The pilot curriculum appeared to successfully introduce physicians to concepts around health care costs and value and initiated important discussions about the role physicians can play in containing costs and promoting value. Additional education, increased cost transparency, and more decision support tools are needed to help physicians translate knowledge into practice.

Socioeconomic Status and Hospitalization Costs for Children with Brain and Spinal Cord Injury.

OBJECTIVE: To determine if household income is associated with hospitalization costs for severe traumatic brain injury (TBI) and spinal cord injury (SCI). STUDY DESIGN: Retrospective cohort study of inpatient, nonrehabilitation hospitalizations at 43 freestanding children's hospitals for patients <19 years old with unintentional severe TBI and SCI from 2009-2012. Standardized cost of care for hospitalizations was modeled using mixed-effects methods, adjusting for age, sex, race/ethnicity, primary payer, presence of chronic medical condition, mechanism of injury, injury severity, distance from residence to hospital, and trauma center level. Main exposure was zip code level median annual household income. RESULTS: There were 1061 patients that met inclusion criteria, 833 with TBI only, 227 with SCI only, and 1 with TBI and SCI. Compared with those with the lowest-income zip codes, patients from the highest-income zip codes were more likely to be older, white (76.7% vs 50.4%), have private insurance (68.9% vs 27.9%), and live closer to the hospital (median distance 26.7 miles vs 81.2 miles). In adjusted models, there was no significant association between zip code level household income and hospitalization costs. CONCLUSIONS: Children hospitalized with unintentional, severe TBI and SCI showed no difference in standardized hospital costs relative to a patient's home zip code level median annual household income. The association between household income and hospitalization costs may vary by primary diagnosis.

Structure and Function of Observation Units in Children's Hospitals: A Mixed-Methods Study.

OBJECTIVE: Observation unit (OU) use has been promoted recently to decrease resource utilization and costs for select patients, but little is known about the operations of pediatric OUs. This study aimed to characterize the infrastructure and function of OUs within freestanding children's hospitals and to compare characteristics between hospitals with and without OUs. METHODS: All 43 freestanding children's hospitals that submit data to the Pediatric Health Information System were contacted in 2013 to identify OUs that admitted unscheduled patients from their emergency department (ED) in 2011. Semistructured interviews were conducted with representatives at hospitals with these OUs. Characteristics of hospitals with and without OUs were compared. RESULTS: Fourteen (33%) of 43 hospitals had an OU during 2011. Hospitals with OUs had more beds and more annual ED visits compared to those without OUs. Most OUs (65%) were located in the ED and had <12 beds (65%). Staffing models and patient populations differed between OUs. Nearly 60% were hybrid OUs, providing scheduled services. OUs lacked uniform outcome measures. Themes included: admissions were intuition based, certain patients were not well suited for OUs, OUs had rapid-turnover cultures, and the designation of observation status was arbitrary. Challenges included patient discontent with copayments and payer-driven utilization reviews. CONCLUSIONS: OUs were located in higher volume hospitals and varied by location, size, and staffing. Most functioned as hybrid OUs. OUs based admissions on intuition, had staffing cultures centered on rapid turnover of patient care, lacked consistent outcome measures, and faced challenges regarding utilization review and patient copayments.