JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded in electronic health record (EHR) environments, yet their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists on how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.

Objective:

This study aimed to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing pMSBOS-TS—an AI-enabled CDSS for personalized surgical blood ordering—prior to large-scale deployment.

Methods:

We conducted a consensual qualitative study with fourteen multidisciplinary healthcare professionals involved in transfusion-related tasks at a large tertiary hospital. Following one pilot focus group to refine the interview guide and workflow diagram, two semi-structured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, supported by task- and workflow-based analysis of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.

Results:

A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering, planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.

Conclusions:

This pre-implementations, SEIPS-based qualitative evaluation suggests that successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights for staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.