JOURNAL ARTICLE
It Takes Two to Make It Right: How Nurses' Response to Sepsis Alerts Impacts Physicians' Process Compliance.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2026, v. 28, n. 1. P. 20 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Ayvaci, Mehmet U. S.; Mobini, Zahra; Özer, Özalp 3 of 3
Abstract
This article examines how nurse-physician interactions, facilitated by an automated sepsis alert system integrated into electronic health records (EHRs), influence physicians' compliance with evidence-based sepsis care standards and subsequent patient outcomes. Using data from a large U.S. hospital system, the study finds that nurses’ timely acknowledgment of alerts and notification of physicians significantly increase physicians’ likelihood of performing recommended diagnostic or treatment actions within designated time frames. This positive effect is amplified under high workload conditions but weakened by frequent false-positive alerts, which contribute to alert fatigue and reduce trust in the system. Improved physician compliance, driven by effective nurse-physician coordination, is associated with shorter hospital stays and fewer intensive care unit admissions. The findings highlight the importance of empowering nurses in clinical workflows and designing alert systems that consider team dynamics, workload, and alert accuracy to enhance care quality.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2026/01, Vol. 28, Issue 1, p20
- Document Type:Conference Paper/Materials
- Subject Area:Social Sciences and Humanities
- Publication Date:2026
- ISSN:1523-4614
- DOI:10.1287/msom.2022.0242
- Accession Number:190748629
- Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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