Health AI Central
Peer-Reviewed Publication
Hosp Pediatr2018;8(9):578-587.September 1, 2018Journal Article

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission.

Louis Ehwerhemuepha1, Stacey Finn2, Michael Rothman3, Cyril Rakovski4, William Feaster5
1Children's Hospital of Orange County, Orange, California; lehwerhemuepha@choc.org.
2Cedar Gate Technologies, Greenwich, Connecticut.
3PeraHealth, Inc, Charlotte, North Carolina; and.
4School of Computational and Data Science, Chapman University, Orange, California.
5Children's Hospital of Orange County, Orange, California.
PubMed DOI

Abstract

OBJECTIVES: To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions. METHODS: Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (p…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.