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  4. A health equity monitoring framework based on process mining
 
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2024
Journal Article
Title

A health equity monitoring framework based on process mining

Abstract
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.
Author(s)
Adams, Jan Niklas
Rheinisch-Westfälische Technische Hochschule Aachen
Ziegler, Jennifer
University of Manitoba
McDermott, Matthew B.A.
Harvard Medical School
Douglas, Molly J.
University of Arizona College of Medicine – Tucson
Eber, Rene
Montpellier Recherche en Management (MRM)
Gichoya, Judy Wawira
Emory University School of Medicine
Goode, Deirdre
Harvard Medical School
Sankaranarayanan, Swami
MIT Computer Science & Artificial Intelligence Laboratory
Chen, Ziyue
A-Star, Genome Institute of Singapore
van der Aalst, Wil M.P.
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Celi, Leo Anthony G.
Massachusetts Institute of Technology
Journal
Plos Digital Health
Funder
Robert Wood Johnson Foundation
Open Access
DOI
10.1371/journal.pdig.0000575
Additional link
Full text
Language
English
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
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