Preventing Pediatric Readmissions: How Does the Hospital Fit In?

Home Health Care Research for Children With Disability and Medical Complexity

Pediatric home health care represents a vital system of care for children with disability and medical complexity, encompassing services provided by family caregivers and nonfamily home health care providers and the use of durable medical equipment and supplies. Home health care is medically necessary for the physiologic health of children with disability and medical complexity and for their participation and function within home, school, and community settings. While the study of pediatric home health care in the United States has increased in the last decade, its research remains primarily methodologically limited to observational studies. Dedicated funding and research efforts are needed to transform American home health care research to address multifaceted outcomes valued by families and providers as well as payers and government programs. In this paper, we review the recent literature in pediatric home health care and then propose an actional agenda that could address its missing evidence base. We posit that pediatricians should partner with family caregiving experts and patients to advance knowledge about child and family health outcomes, home health care use, new models of care, and optimal approaches to education and training while also considering meaningful approaches to address disparities. The creation of an American pediatric home health care data-sharing consortium, patient registry, and reproducible access and quality measures is also needed. Most importantly, efforts should center on patient- and family-centered health priorities, with the goal of ensuring equitable outcomes for every child and family.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years

IMPORTANCE

Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.

OBJECTIVE

To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.

DESIGN, SETTING, AND PARTICIPANTS

This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.

MAIN OUTCOMES AND MEASURES

Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.

RESULTS

The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.

CONCLUSIONS AND RELEVANCE

In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Disease Severity and Risk Factors of 30-Day Hospital Readmission in Pediatric Hospitalizations for Pneumonia

Pneumonia is the leading cause of hospitalization in pediatric patients. Disease severity greatly influences pneumonia progression and adverse health outcomes such as hospital readmission. Hospital readmissions have become a measure of healthcare quality to reduce excess expenditures. The aim of this study was to examine 30-day all-cause readmission rates and evaluate the association between pneumonia severity and readmission among pediatric pneumonia hospitalizations. Using 2018 Nationwide Readmissions Database (NRD), we conducted a cross-sectional study of pediatric hospitalizations for pneumonia. Pneumonia severity was defined by the presence of respiratory failure, sepsis, mechanical ventilation, dependence on long-term supplemental oxygen, and/or respiratory intubation. Outcomes of interest were 30-day all-cause readmission, length of stay, and cost. The rate of 30-day readmission for the total sample was 5.9%, 4.7% for non-severe pneumonia, and 8.7% for severe pneumonia (p < 0.01). Among those who were readmitted, hospitalizations for severe pneumonia had a longer length of stay (6.5 vs. 5.4 days, p < 0.01) and higher daily cost (USD 3246 vs. USD 2679, p < 0.01) than admissions for non-severe pneumonia. Factors associated with 30-day readmission were pneumonia severity, immunosuppressive conditions, length of stay, and hospital case volume. To reduce potentially preventable readmissions, clinical interventions to improve the disease course and hospital system interventions are necessary.

A Quality Improvement Intervention Bundle to Reduce 30-Day Pediatric Readmissions

Supplemental Digital Content is available in the text.

Introduction:

Pediatric hospital readmissions can represent gaps in care quality between discharge and follow-up, including social factors not typically addressed by hospitals. This study aimed to reduce the 30-day pediatric readmission rate on 2 general pediatric services through an intervention to enhance care spanning the hospital stay, discharge, and follow-up process.

Methods:

A multidisciplinary team developed an intervention bundle based on a needs assessment and evidence-based models of transitional care. The intervention included pre-discharge planning with a transition coordinator, screening and intervention for adverse social determinants of health (SDH), medication reconciliation after discharge, communication with the primary care provider, access to a hospital-based transition clinic, and access to a 24-hour direct telephone line staffed by hospital attending pediatricians. These were implemented sequentially from October 2013 to February 2017. The primary outcome was the readmission rate within 30 days of index discharge. The length of stay was a balancing measure.

Results:

During the intervention, the included services discharged 4,853 children. The pre-implementation readmission rate of 10.3% declined to 7.4% and remained stable during a 4-month post-intervention observation period. Among 1,394 families screened for adverse SDH, 48% reported and received assistance with ≥ 1 concern. The length of stay increased from 4.10 days in 2013 to 4.30 days in 2017.

Conclusions:

An intervention bundle, including SDH, was associated with a sustained reduction in readmission rates to 2 general pediatric services. Transitional care that addresses multiple domains of family need during a child’s health crisis can help reduce pediatric readmissions.

Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study

Background

Gaining a better understanding of the probability, timing and prediction of rehospitalisation amongst preterm babies could help improve outcomes. There is limited research addressing these topics amongst extremely and very preterm babies. In this context, unplanned rehospitalisations constitute an important, potentially modifiable adverse event. We aimed to establish the probability, time-distribution and predictability of unplanned rehospitalisation within 30 days of discharge in a population of French preterm babies.

Methods

This study used data from EPIPAGE 2, a population-based prospective study of French preterm babies. Only those babies discharged home alive and whose parents responded to the one-year survey were eligible for inclusion in our study. For Kaplan-Meier analysis, the outcome was unplanned rehospitalisation censored at 30 days. For predictive modelling, the outcome was binary, recording unplanned rehospitalisation within 30 days of discharge. Predictors included routine clinical variables selected based on expert opinion.

Results

Of 3841 eligible babies, 350 (9.1, 95% CI 8.2–10.1) experienced an unplanned rehospitalisation within 30 days. The probability of rehospitalisation progressed at a consistent rate over the 30 days. There were significant differences in rehospitalisation probability by gestational age. The cross-validated performance of a ten predictor model demonstrated low discrimination and calibration. The area under the receiver operating characteristic curve was 0.62 (95% CI 0.59–0.65).

Conclusions

Unplanned rehospitalisation within 30 days of discharge was infrequent and the probability of rehospitalisation progressed at a consistent rate. Lower gestational age increased the probability of rehospitalisation. Predictive models comprised of clinically important variables had limited predictive ability.

Progress (?) Toward Reducing Pediatric Readmissions

Many children's hospitals are actively working to reduce readmissions to improve care and avoid financial penalties. We sought to determine if pediatric readmission rates have changed over time. We used data from 66 hospitals in the Inpatient Essentials Database including index hospitalizations from January, 2010 through June, 2016. Seven-day all cause (AC) and potentially preventable readmission (PPR) rates were calculated using 3M PPR software. Total and condition-specific quarterly AC and PPR rates were generated for each hospital and in aggregate. We included 4.52 million hospitalizations across all study years. Readmission rates did not vary over the study period. The median seven-day PPR rate across all quarters was 2.5% (range 2.1%-2.5%); the median seven-day AC rate across all quarters was 5.1% (range 4.3%-5.3%). Readmission rates for individual conditions fluctuated. Despite significant national efforts to reduce pediatric readmissions, both AC and PPR readmission rates have remained unchanged over six years.

Lost Earnings and Nonmedical Expenses of Pediatric Hospitalizations

BACKGROUND AND OBJECTIVES

Hospitalization-related nonmedical costs, including lost earnings and expenses such as transportation, meals, and child care, can lead to challenges in prioritizing postdischarge decisions. In this study, we quantify such costs and evaluate their relationship with sociodemographic factors, including family-reported financial and social hardships.

METHODS

This was a cross-sectional analysis of data collected during the Hospital-to-Home Outcomes Study, a randomized trial designed to determine the effects of a nurse home visit after standard pediatric discharge. Parents completed an in-person survey during the child's hospitalization. The survey included sociodemographic characteristics of the parent and child, measures of financial and social hardship, household income and also evaluated the family's total nonmedical cost burden, which was defined as all lost earnings plus expenses. A daily cost burden (DCB) standardized it for a 24-hour period. The daily cost burden as a percentage of daily household income (DCBi) was also calculated.

RESULTS

Median total cost burden for the 1372 households was $113, the median DCB was $51, and the median DCBi was 45%. DCB and DCBi varied across many sociodemographic characteristics. In particular, single-parent households (those with less work flexibility and more financial hardships experienced significantly higher DCB and DCBi. Those who reported ≥3 financial hardships lost or spent 6-times more of their daily income on nonmedical costs than those without hardships. Those with ≥1 social hardships lost or spent double their daily income compared with those without social hardships.

CONCLUSIONS

Nonmedical costs place burdens on families of children who are hospitalized, disproportionately affecting those with competing socioeconomic challenges.