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

ABSTRACT: 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.

SUBMITTER: deJong NA

PROVIDER: S-EPMC7190252 | biostudies-literature | 2020 Mar-Apr

REPOSITORIES: biostudies-literature

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

deJong Neal A NA Kimple Kelly S KS Morreale Madlyn C MC Hang Shona S Davis Darragh D Steiner Michael J MJ

Pediatric quality & safety 20200228 2

<h4>Introduction</h4>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.<h4>Methods</h4>A multidisciplinary team developed an intervention bundle based on a needs assessment and evidence-bas ...[more]

PMID: 32426630

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Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

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

Publications

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

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