A health equity monitoring framework based on process mining.


Journal

PLOS digital health
ISSN: 2767-3170
Titre abrégé: PLOS Digit Health
Pays: United States
ID NLM: 9918335064206676

Informations de publication

Date de publication:
Aug 2024
Historique:
received: 01 12 2023
accepted: 08 07 2024
medline: 29 8 2024
pubmed: 29 8 2024
entrez: 28 8 2024
Statut: epublish

Résumé

In the United States, there is a proposal to link hospital Medicare payments with health equity measures, signaling a need to precisely measure equity in healthcare delivery. Despite significant research demonstrating disparities in health care outcomes and access, there is a noticeable gap in tools available to assess health equity across various health conditions and treatments. The available tools often focus on a single area of patient care, such as medication delivery, but fail to examine the entire health care process. The objective of this study is to propose a process mining framework to provide a comprehensive view of health equity. Using event logs which track all actions during patient care, this method allows us to look at disparities in single and multiple treatment steps, but also in the broader strategy of treatment delivery. We have applied this framework to the management of patients with sepsis in the Intensive Care Unit (ICU), focusing on sex and English language proficiency. We found no significant differences between treatments of male and female patients. However, for patients who don't speak English, there was a notable delay in starting their treatment, even though their illness was just as severe and subsequent treatments were similar. This framework subsumes existing individual approaches to measure health inequities and offers a comprehensive approach to pinpoint and delve into healthcare disparities, providing a valuable tool for research and policy-making aiming at more equitable healthcare.

Identifiants

pubmed: 39196891
doi: 10.1371/journal.pdig.0000575
pii: PDIG-D-23-00451
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0000575

Informations de copyright

Copyright: © 2024 Adams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health.

Auteurs

Jan Niklas Adams (JN)

Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany.
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Jennifer Ziegler (J)

Department of Internal Medicine, Section of Critical Care, University of Manitoba, Winnipeg, Manitoba, Canada.

Matthew McDermott (M)

Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.

Molly J Douglas (MJ)

Department of Surgery, University of Arizona, Tucson, Arizona, United States of America.

René Eber (R)

Montpellier Research in Management, Montpellier University, Montpellier, France.

Judy Wawira Gichoya (JW)

Department of Radiology & Imaging Sciences, Emory University, Atlanta, Georgia, United States of America.

Deirdre Goode (D)

Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.

Swami Sankaranarayanan (S)

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Ziyue Chen (Z)

Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore.

Wil M P van der Aalst (WMP)

Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany.
Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany.

Leo Anthony Celi (LA)

Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.

Classifications MeSH