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Development of a pediatric readmissions encounter predictor: Benchmarks for 30-day unplanned pediatric readmission
Greg Attard1, James C Gay2, Katherine A Auger3,4
1Children's Hospital Association, Lenexa, Kansas, USA.
Insights
Hospitals can now use the Pediatric Readmissions Encounter Predictor (PREP) to benchmark pediatric readmissions. This validated model helps identify improvement opportunities by predicting expected readmission rates.
Area of Science:
- Healthcare Analytics
- Pediatric Hospital Medicine
- Health Services Research
Background:
- Pediatric readmissions are critical quality metrics for hospitals.
- Establishing accurate benchmarks is essential for contextualizing readmission rates.
- Current benchmarks may not adequately reflect pediatric-specific factors.
Purpose of the Study:
- To develop and validate a predictive model for pediatric readmissions.
- To create reliable benchmarks for 30-day unplanned pediatric readmissions.
- To aid hospitals in identifying areas for quality improvement.
Main Methods:
- Utilized administrative data from the National Readmission Database (2019).
- Employed All-Patient Refined Diagnosis-Related Groups (APR-DRGs) with severity of illness subclasses.
- Validated the model using 2018 data from the same database.
Main Results:
- The overall 30-day unplanned pediatric readmission rate was 2.5%.
- The Pediatric Readmissions Encounter Predictor (PREP) demonstrated acceptable discriminatory performance (AUC=0.738).
- The model showed good calibration across predicted probability levels.
Conclusions:
- PREP is a validated tool for benchmarking pediatric readmissions.
- This model can assist hospitals in identifying opportunities to reduce readmissions.
- PREP supports data-driven quality improvement initiatives in pediatric care.
Abstract:
Pediatric readmissions are important for hospitals to measure and monitor to identify potential improvement opportunities but require benchmarks to contextualize observed and expected readmissions. We developed and validated the Pediatric Readmissions Encounter Predictor (PREP) in administrative data as pediatric readmission benchmarks using the All-Patient Refined Diagnosis-Related Groups with severity of illness subclasses. We developed the model using data from the National Readmission Database in 2019. We subsequently validated these models in the same data set from 2018. The overall 30-day unplanned readmission rate was 2.5%. The model demonstrated acceptable discriminatory performance (area under the receiver operator characteristic curve [AUC] = 0.738, 95% confidence interval [CI]: 0.734, 0.741) and was well calibrated across all levels of predicted probabilities. PREP is a promising readmission prediction model which hospitals can use to assist in identifying improvement opportunities.
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