Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 20, 2025
Date Accepted: Dec 24, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
SEIPS-Based Consensual Qualitative Study on Healthcare Professionals Perceptions of a New Clinical Workflow System
ABSTRACT
Background:
This consensual qualitative study aimed to examine the anticipated impacts of personalized Maximum Surgical Blood Order Schedule–Thoracic Surgery (pMSBOS-TS), a machine learning–based decision support system designed to predict personalized maximum surgical blood ordering, within an electronic health record (EHR) environment.
Objective:
To qualitatively examine the anticipated clinical, organizational, and workflow implications of implementing an artificial intelligence-powered clinical decision support system for personalized maximum surgical blood ordering within an EHR environment prior to large-scale deployment.
Methods:
Fourteen multidisciplinary clinicians at a tertiary care hospital participated in two semi-structured focus group discussions following a pilot interview. Data were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, which categorizes findings across People, Environment, Tools, and Tasks, with member checking conducted to ensure validity.
Results:
A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and seven overarching domains. Participants anticipated reduced variation in blood ordering practices and improved forecasting of transfusion demand, conditional on predictive accuracy, user-friendly interfaces, and seamless integration into existing EHR workflows. Concerns included increased verification burden, limitations in unexpected clinical scenarios, and institutional cultural barriers.
Conclusions:
These findings suggest that successful adoption will depend on sociotechnical readiness, user trust, and phased implementation strategies to mitigate additional workload and patient safety risks.
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Copyright
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