Development and Optimization of the Veterans Affairs' National Heart Failure Dashboard for Population Health Management.

Autor: Brownell N; Division of Cardiology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA., Kay C; VA Pharmacy Benefits Management Academic Detailing Services, Hines, IL., Parra D; Veterans Integrated Service Network 8, Pharmacy Benefits Management, Department of Veterans Affairs, Tampa, FL., Anderson S; Veterans Affairs, Gainesville, FL., Ballister B; Center for Medication Safety, VA Pharmacy Benefits Management Services, Hines VA, Hines, IL., Cave B; VA West Palm Beach Medical Center, West Palm Beach, FL., Conn J; Northern Arizona VA Health Care System, Prescott, AZ., Dev S; Southern Arizona VA Health Care System, Tucson, AZ., Kaiser S; Orlando VA Medical Center, Orlando, FL., ROGERs J; Department of Veterans Affairs, Jacksonville, FL., Touloupas AD; Louisville VA Medical Center, Louisville, KY., Verbosky N; James A. Haley Veterans' Hospital, Tampa, FL., Yassa NM; Bay Pines VA Healthcare System, Bay Pines, FL., Young E; VA Sierra Pacific Network (VISN 21) Clinical Resource Hub, Palo Alto, CA., Ziaeian B; Division of Cardiology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA. Electronic address: BZiaeian@mednet.ucla.edu.
Jazyk: angličtina
Zdroj: Journal of cardiac failure [J Card Fail] 2024 Mar; Vol. 30 (3), pp. 452-459. Date of Electronic Publication: 2023 Sep 25.
DOI: 10.1016/j.cardfail.2023.08.024
Abstrakt: Background: In 2020, the Veterans Affairs (VA) health care system deployed a heart failure (HF) dashboard for use nationally. The initial version was notably imprecise and unreliable for the identification of HF subtypes. We describe the development and subsequent optimization of the VA national HF dashboard.
Materials and Methods: This study describes the stepwise process for improving the accuracy of the VA national HF dashboard, including defining the initial dashboard, improving case definitions, using natural language processing for patient identification, and incorporating an imaging-quality hierarchy model. Optimization further included evaluating whether to require concurrent ICD-codes for inclusion in the dashboard and assessing various imaging modalities for patient characterization.
Results: Through multiple rounds of optimization, the dashboard accuracy (defined as the proportion of true results to the total population) was improved from 54.1% to 89.2% for the identification of HF with reduced ejection fraction (HFrEF) and from 53.9% to 88.0% for the identification of HF with preserved ejection fraction (HFpEF). To align with current guidelines, HF with mildly reduced ejection fraction (HFmrEF) was added to the dashboard output with 88.0% accuracy.
Conclusions: The inclusion of an imaging-quality hierarchy model and natural-language processing algorithm improved the accuracy of the VA national HF dashboard. The revised dashboard informatics algorithm has higher use rates and improved reliability for the health management of the population.
Competing Interests: Disclosures The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
(Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: