Bringing AI to the OR: integrating a machine learning predictive model in the EHR – a pilot on user-friendliness

Abstract

OBJECTIVES: Evaluate the technical integration and usability of an intraoperative predictive machine learning model for colorectal anastomotic leakage within the Epic electronic health records (EHRs) at a single academic centre, with outputs blinded. METHODS: The system used 28 data elements from patient records, intraoperative monitoring equipment and structured operating room (OR) observations. Data were collected every 15 min and processed in the cloud with encrypted, pseudonymised transfer. Usability was assessed using the System Usability Scale (SUS) and additional questions addressing access, clarity, responsiveness, workflow impact, safety and training needs. Convenience sampling was used, with all available OR staff involved in eligible procedures invited to participate. RESULTS: 15 procedures (9 October 2024 to 6 March 2025) were included. Nine unique users responded (≈75% of 12 exposed; four surgeons, five OR assistants). The interface was accessed in all cases, and predictions were generated each time from all three sources. Mean SUS was 79.2 (SD 10.4; 95% CI 71.2 to 87.2). Diagnostic items favoured access speed and clarity; prediction responsiveness scored lower. DISCUSSION: The system could be implemented and operated reliably within a single centre and EHR environment and was perceived as easy to use, despite the small sample size. However, findings may not generalise to other hospitals or EHRs without adaptation and further multi-site evaluation. CONCLUSION: The system functioned reliably and was positively received, supporting readiness for activation in real-time clinical use and prospective evaluation. Future deployment should incorporate regulatory planning and a quality-management framework to monitor performance, safety and changes in model behaviour over time.

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