โก Quick Summary
This study explored the perceptions of health care professionals regarding the implementation of a new AI-enabled clinical decision support system (CDSS) for personalized surgical blood ordering. Findings suggest that while the system could enhance efficiency, concerns about workflow integration and communication challenges must be addressed for successful adoption.
๐ Key Details
- ๐ Participants: 14 multidisciplinary health care professionals (5 physicians, 6 nurses, 3 blood bank staff)
- โ๏ธ Technology: Personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS)
- ๐ Methodology: Consensual qualitative research using focus group discussions
- ๐ Framework: Systems Engineering Initiative for Patient Safety (SEIPS) 101
๐ Key Takeaways
- ๐ค AI Integration: The pMSBOS-TS system has the potential to reduce unwarranted variation in blood ordering.
- โ ๏ธ Concerns: Increased verification burden and potential communication bottlenecks were highlighted by participants.
- ๐ฅ Organizational Factors: Culture and governance structures are critical for successful implementation.
- ๐ Workflow Fit: The system’s effectiveness depends on its integration with existing EHR workflows.
- ๐ฃ๏ธ User Trust: Building trust among users is essential for the adoption of AI-enabled systems.
- ๐ Predictive Performance: The reliability of the algorithm is crucial for its acceptance in clinical settings.
- ๐ Study Location: Conducted at a large tertiary hospital.
- ๐ Publication: J Med Internet Res, 2026; 28:e86166.
๐ Background
The integration of artificial intelligence (AI) into healthcare, particularly through clinical decision support systems (CDSSs), is transforming clinical workflows. However, the introduction of such technologies can disrupt existing processes and raise concerns about patient safety, especially in high-stakes environments like surgical transfusion. Understanding the perceptions of frontline professionals is vital for successful implementation.
๐๏ธ Study
This qualitative study aimed to assess the anticipated implications of the pMSBOS-TS system among health care professionals involved in transfusion-related tasks. Through two semistructured focus group discussions, researchers gathered insights from 14 participants, analyzing the data using the SEIPS 101 framework to focus on key elements such as People, Environment, Tools, and Tasks.
๐ Results
The analysis revealed a total of 189 semantic units and 61 core ideas across various domains. Participants expressed optimism about the potential of pMSBOS-TS to streamline blood ordering processes, contingent upon reliable algorithm performance and seamless integration into existing workflows. However, they also raised concerns about the increased verification burden and potential communication issues between clinical units and the blood bank.
๐ Impact and Implications
The findings from this study underscore the importance of addressing both technical and sociocultural factors in the implementation of AI-enabled CDSSs. By focusing on user trust, workflow fit, and organizational support, healthcare institutions can enhance the likelihood of successful integration of predictive transfusion systems into surgical workflows, ultimately improving patient safety and care quality.
๐ฎ Conclusion
This research highlights the critical role of health care professionals’ perceptions in the adoption of AI technologies in clinical settings. The successful implementation of systems like pMSBOS-TS hinges not only on their predictive capabilities but also on the readiness of the healthcare environment to embrace such innovations. Continued exploration and dialogue in this area will be essential for advancing patient safety and operational efficiency in healthcare.
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Assessing Health Care Professionals’ Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research.
Abstract
BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.
OBJECTIVE: This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.
METHODS: We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured 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, and were supported by task- and workflow-based analyses 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 and 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 preimplementation, SEIPS-based qualitative evaluation suggests that the 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 to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.
Author: [‘Park YE’, ‘Ock M’, ‘Lee JH’, ‘Ko DH’, ‘Lee HJ’, ‘Park T’, ‘Yoo J’, ‘Lee Y’]
Journal: J Med Internet Res
Citation: Park YE, et al. Assessing Health Care Professionals’ Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research. Assessing Health Care Professionals’ Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research. 2026; 28:e86166. doi: 10.2196/86166