Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.

ABSTRACT:

Background

We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system.

Objective

To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF.

Methods

We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique in

SUBMITTER: Garvin JH 

PROVIDER: S-EPMC5789165 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature