Wang, Y. orcid.org/0000-0003-1575-0245, Ji, J., Zhang, M. et al. (1 more author) (Accepted: 2025) Does workforce diversity, equity, and inclusion prevent patient safety incidents: a double machine learning approach. Journal of the Association for Information Systems. ISSN 1558-3457 (In Press)
Abstract
Workforce diversity, equity, and inclusion (DEI) are increasingly recognized as essential components in healthcare organizations. However, the academic landscape lacks robust empirical research on how workforce DEI influences patient safety outcomes, particularly regarding the boundary conditions that might moderate this relationship. This study analyzes a longitudinal dataset from 2017 to 2021, which includes DEI metrics, staff-reported patient safety incidents, and employee feedback on DEI from Glassdoor and Indeed for 120 NHS Trusts in England’s acute care sector. Workforce DEI is examined through both demographic and experiential aspects to provide a comprehensive view. Employing a double machine learning approach, our findings demonstrate that a unit increase in workforce DEI scores is associated with a reduction of 8.108 patient safety incidents per 1000 admissions. Moreover, regions with greater patient racial diversity and healthcare organizations with lower complexity experience significantly enhanced benefits from DEI initiatives. This study provides healthcare policymakers and institutions with actionable insights for strategically tailoring DEI initiatives to effectively improve patient safety.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2025 by the Association for Information Systems. |
Keywords: | Diversity, equity, and inclusion (DEI); Patient safety; double machine learning; healthcare analytics |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Mar 2025 10:54 |
Last Modified: | 07 Mar 2025 10:57 |
Status: | In Press |
Publisher: | Association for Information Systems |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223930 |
Download
Filename: JAIS_Manuscript_Final_proof.pdf
