The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions.

ABSTRACT:

Background

Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance.

Objectives

To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection.

Methods

Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison

SUBMITTER: Walsh C 

PROVIDER: S-EPMC4261028 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature