JOURNAL ARTICLE
Impacts of Care Provider Collaborations on the Service Time for Inpatient Stays: An Analysis Using Electronic Health Record Audit Logs and Dynamic Graphs.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2026, v. 28, n. 1. P. 117 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Jetley, Gaurav; Zhang, He 3 of 3
Abstract
This article investigates the impact of real-time collaboration among care providers on inpatient length of stay (LOS) using electronic health record (EHR) audit logs from a large U.S. teaching hospital. Collaboration is defined as temporally proximate coaccesses of patient records during an inpatient stay, distinct from team familiarity, which reflects accumulated prior experience. The study employs a temporal data-mining algorithm to construct dynamic collaboration networks and finds a nonlinear, inverted U-shaped relationship between collaboration strength and LOS: weak initial collaboration increases LOS, but after approximately two to four prior interactions (the tipping point), stronger collaboration reduces LOS. Additionally, high variability in collaboration strength among providers correlates with longer LOS, suggesting that consistent collaboration across all team members enhances efficiency. These findings highlight the operational importance of fostering strong, uniform collaboration patterns to improve inpatient service efficiency and potentially reduce readmission rates.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2026/01, Vol. 28, Issue 1, p117
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2026
- ISSN:1523-4614
- DOI:10.1287/msom.2022.0176
- Accession Number:190748626
- 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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