Evidence map›Paper›PMID 39196891›Full record

ArticlePLOS digital health2024

A health equity monitoring framework based on process mining.

Jan Niklas Adams, Jennifer Ziegler, Matthew McDermott, Molly J Douglas, René Eber, Judy Wawira Gichoya, Deirdre Goode, Swami Sankaranarayanan, Ziyue Chen, Wil M P van der Aalst and 1 more

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jan Niklas AdamsChair of Process and Data Science, RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0000-0001-8954-4925
Jennifer ZieglerDepartment of Internal Medicine, Section of Critical Care, University of Manitoba, Winnipeg, Manitoba, Canada.
Matthew McDermottDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
Molly J DouglasDepartment of Surgery, University of Arizona, Tucson, Arizona, United States of America.ORCID https://orcid.org/0000-0002-2567-3274
René EberMontpellier Research in Management, Montpellier University, Montpellier, France.
Judy Wawira GichoyaDepartment of Radiology & Imaging Sciences, Emory University, Atlanta, Georgia, United States of America.
Deirdre GoodeDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
Swami SankaranarayananComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Ziyue ChenGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore.
Wil M P van der AalstChair of Process and Data Science, RWTH Aachen University, Aachen, Germany.
Leo Anthony CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID39196891
PMCPMC11355534

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.