<p>The ability for our health care system to adapt with extraordinary speed under crisis has never been more evident than now as we face the coronavirus disease 2019 (COVID-19) pandemic. Ambulatory clinics and primary care providers have seen shifts in patient demand for their services, and elective and scheduled care at hospitals has been reduced to mitigate the spread of infection. Almost overnight, the health care system has shifted toward providing care through telehealth platforms to avoid the catastrophic consequences of “doing business as usual,” making telehealth a leading modality of health care delivery.</p>

<p><strong>BACKGROUND AND OBJECTIVES: </strong>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.</p>

<p><strong>METHODS: </strong>Retrospective cohort study of hospitalized children (age &lt;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.</p>

<p><strong>RESULTS: </strong>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 ( &lt; .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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>OBJECTIVE: </strong>(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.</p>

<p><strong>DESIGN/SETTING: </strong>Retrospective cohort study of hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 children</p>

<p><strong>MEASUREMENTS:</strong> Variation in trimmed standardized costs were assessed within and across regions. Linear mixed effects models were adjusted for patient- and encounter-level variables to assess drivers of variation.</p>

<p><strong>RESULTS:</strong> After adjusting for patient-level factors, variation remained. Using census division clusters, mean trimmed and 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 compared with the lowest-cost region. Comparing hospitals in the same region, standardized costs were significantly different (P &lt; 0.001) for each condition in each region. Drivers of variation were encounter-level variables including length of stay and intensive care unit utilization. For this cohort, annual savings from reducing variation would equal $69.1 million at the interregional level and $25.2 million at the intraregional level.</p>

<p><strong>CONCLUSIONS:</strong> Pediatric hospital costs vary between and within regions. Future studies should examine how much of this variation is avoidable. To the extent that less spending does not compromise outcomes, care models may be adjusted to eliminate unwarranted variation and reduce costs.</p>

<p><strong>OBJECTIVES: </strong>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.</p>

<p><strong>STUDY DESIGN: </strong>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.</p>

<p><strong>RESULTS: </strong>For the 458 686 discharges meeting PACR inclusion criteria, in multivariable models, factors associated with higher discharge-level PACR measure included age &lt;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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>IMPORTANCE: </strong>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.</p>

<p><strong>OBJECTIVE: </strong>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.</p>

<p><strong>DESIGN, SETTING, AND PARTICIPANTS: </strong>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.</p>

<p><strong>EXPOSURES: </strong>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.</p>

<p><strong>MAIN OUTCOMES AND MEASURES: </strong>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.</p>

<p><strong>RESULTS: </strong>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).</p>

<p><strong>CONCLUSIONS AND RELEVANCE: </strong>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.</p>

<p><strong>OBJECTIVE: </strong>To determine if household income is associated with hospitalization costs for severe traumatic brain injury (TBI) and spinal cord injury (SCI).</p>

<p><strong>STUDY DESIGN: </strong>Retrospective cohort study of inpatient, nonrehabilitation hospitalizations at 43 freestanding children's hospitals for patients &lt;19&nbsp;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.</p>

<p><strong>RESULTS: </strong>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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>BACKGROUND AND OBJECTIVE: </strong>Treating envenomation with antivenom is costly. Many patients being treated with antivenom are in observation status, a billing designation for patients considered to need care that is less resource-intensive, and less expensive, than inpatient care. Observation status is also associated with lower hospital reimbursements and higher patient cost-sharing. The goal of this study was to examine resource utilization for treatment of envenomation under observation and inpatient status, and to compare patients in observation status receiving antivenom with all other patients in observation status.</p>

<p><strong>METHODS: </strong>This was a retrospective study of patients with a primary diagnosis of toxic effect of venom seen during 2009 at 33 freestanding children's hospitals in the Pediatric Health Information System. Data on age, length of stay, adjusted costs (ratio cost to charges), ICU flags, and antivenom utilization were collected. Comparisons were conducted according to admission status (emergency department only, observation status, and inpatient status), and between patients in observation status receiving antivenom and patients in observation status with other diagnoses.</p>

<p><strong>RESULTS: </strong>A total of 2755 patients had a primary diagnosis of toxic effect of venom. Of the 335 hospitalized, either under observation (n = 124) or inpatient (n = 211) status, 107 (31.9%) received antivenom. Of those hospitalized patients receiving antivenom, 24 (22.4%) were designated as observation status. Costs were substantially higher for patients who received antivenom and were driven by pharmacy costs (mean cost: $17 665 for observation status, $20 503 for inpatient status). Mean costs for the 47 162 patients in observation status with other diagnoses were $3001 compared with $17 665 for observation-status patients who received antivenom.</p>

<p><strong>CONCLUSIONS: </strong>Treatment of envenomation with antivenom represents a high-cost outlier within observation-status hospitalizations. Observation status can have financial consequences for hospitals and patients.</p>

<p><strong>BACKGROUND AND OBJECTIVE: </strong>Observation status, in contrast to inpatient status, is a billing designation for hospital payment. Observation-status stays are presumed to be shorter and less resource-intensive, but utilization for pediatric observation-status stays has not been studied. The goal of this study was to describe resource utilization characteristics for patients in observation and inpatient status in a national cohort of hospitalized children in the Pediatric Health Information System.</p>

<p><strong>METHODS: </strong>This study was a retrospective cohort from 2010 of observation- and inpatient-status stays of ≤2 days; all children were admitted from the emergency department. Costs were analyzed and described. Comparison between costs adjusting for age, severity, and length of stay were conducted by using random-effect mixed models to account for clustering of patients within hospitals.</p>

<p><strong>RESULTS: </strong>Observation status was assigned to 67 230 (33.3%) discharges, but its use varied across hospitals (2%-45%). Observation-status stays had total median costs of $2559, including room costs and $678 excluding room costs. Twenty-five diagnoses accounted for 74% of stays in observation status, 4 of which were used for detailed analyses: asthma (n = 6352), viral gastroenteritis (n = 4043), bronchiolitis (n = 3537), and seizure (n = 3289). On average, after risk adjustment, observation-status stays cost $260 less than inpatient-status stays for these select 4 diagnoses. Large overlaps in costs were demonstrated for both types of stay.</p>

<p><strong>CONCLUSIONS: </strong>Variability in use of observation status with large overlap in costs and potential lower reimbursement compared with inpatient status calls into question the utility of segmenting patients according to billing status and highlights a financial risk for institutions with a high volume of pediatric patients in observation status.</p>

<p><strong>OBJECTIVE: </strong>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.</p>

<p><strong>METHODS: </strong>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.</p>

<p><strong>RESULTS: </strong>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 &lt;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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>BACKGROUND: </strong>Pediatric observation units (OUs) have demonstrated reductions in lengths of stay (LOS) and costs of care. Hospital-level outcomes across all observation-status stays have not been evaluated in relation to the presence of a dedicated OU in the hospital.</p>

<p><strong>OBJECTIVE: </strong>To compare observation-status stay outcomes in hospitals with and without a dedicated OU.</p>

<p><strong>DESIGN: </strong>Cross-sectional analysis of hospital administrative data.</p>

<p><strong>METHODS: </strong>Observation-status stay outcomes were compared in hospitals with and without a dedicated OU across 4 categories: (1) LOS, (2) standardized costs, (3) conversion to inpatient status, and (4) return care.</p>

<p><strong>SETTING/PATIENTS: </strong>Observation-status stays in 31 free-standing children's hospitals contributing observation patient data to the Pediatric Health Information System database, 2011.</p>

<p><strong>RESULTS: </strong>Fifty-one percent of the 136,239 observation-status stays in 2011 occurred in 14 hospitals with a dedicated OU; the remainder were in 17 hospitals without. The percentage of observation-status same-day discharges was higher in hospitals with a dedicated OU compared with hospitals without (23.8 vs 22.1, P &lt; 0.001), but risk-adjusted LOS in hours and total standardized costs were similar. Conversion to inpatient status was higher in hospitals with a dedicated OU (11.06%) compared with hospitals without (9.63%, P &lt; 0.01). Adjusted odds of return visits and readmissions were comparable.</p>

<p><strong>CONCLUSIONS: </strong>The presence of a dedicated OU appears to have an influence on same-day and morning discharges across all observation-status stays without impacting other hospital-level outcomes. Inclusion of location of care (eg, dedicated OU, inpatient unit, emergency department) in hospital administrative datasets would allow for more meaningful comparisons of models of hospital care.</p>