Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data.

ABSTRACT:

Importance

Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.

Objective

To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.

Design, setting, and participants

We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. Fro

SUBMITTER: Blecker S 

PROVIDER: S-EPMC5289894 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature