Predicting Hospital Readmissions from Home Healthcare in Medicare Beneficiaries.

ABSTRACT:

Objective

To use patient-level clinical variables to develop and validate a parsimonious model to predict hospital readmissions from home healthcare (HHC) in Medicare fee-for-service beneficiaries.

Design

Retrospective analysis using multivariable logistic regression and gradient boosting machine (GBM) learning to develop and validate a predictive model.

Setting/participants/meaurements

A 5% national sample of patients, aged 65 years or older, with Medicare fee-for-service who received skilled HHC services within 5 days of hospital discharge in 2012 (n = 43 407). Multiple data sets were merged, including Medicare Outcome and Assessment Information Set, Home Health Claims, Medicare Provider Analysis and Review, and Master Beneficiary Summary Files, to extract patient-l

SUBMITTER: Jones CD 

PROVIDER: S-EPMC7323864 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature