Mining high-dimensional administrative claims data to predict early hospital readmissions.

ABSTRACT:

Background

Current readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70).

Objective

To develop an administrative claim-based algorithm to predict 30-day readmission using standardized billing codes and basic admission characteristics available before discharge.

Materials and methods

The algorithm works by exploiting high-dimensional information in administrative claims data and automatically selecting empirical risk factors. We applied the algorithm to index admissions in two types of hospitalized patient: (1) medical patients and (2) patients with chronic pancreatitis (CP). We trained the models on 26,091 medical admissions and 3218 CP a

SUBMITTER: He D 

PROVIDER: S-EPMC3932470 | biostudies-literature | 2014 Mar-Apr

REPOSITORIES: biostudies-literature