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Implementation determinants of a planned machine learning-enabled surgical scheduling system in a high-volume
Isabella Herrington1, Julie Makarski1, Divya Mathura1
1Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.
Objectives:
Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room (OR) resources and affecting patient outcomes and healthcare spending. Manual scheduling systems in Ontario orthopaedic centres create wide variations in wait times, with recent declines in meeting benchmark targets despite increased procedure volumes. Challenges stem from fragmented referral processes, outdated scheduling methods and resource constraints. Artificial intelligence and machine learning (ML) offer potential solutions for optimising scheduling; however, their implementation remains inconsistent. This study aims to identify determinants affecting the rollout of a new ML-driven automated scheduling system at a high-volume elective orthopaedic surgery centre.
Design:
A qualitative description approach supported by implementation science frameworks.
Setting:
A high-volume elective orthopaedic surgery unit at a Canadian tertiary care centre.
Participants:
17 individuals from clinical, administrative and leadership roles who were directly involved in surgical scheduling.
Interventions:
A new ML-driven automated surgical scheduling system.
Outcomes:
Perceptions of the proposed new surgical scheduling system (barriers and enablers of implementation, recommendations for improvement).
Results:
Three main themes were identified, capturing challenges and enablers in the existing scheduling system: system functionality, process-related factors and resource constraints.Participants described substantial inefficiencies in the existing manual scheduling system, including outdated software, fragmented information systems, inconsistent communication and resource constraints. Across interest-holder groups, there was broad but variable perceived support for a planned ML-enabled scheduling system, particularly for improving duration prediction, access to scheduling data and reporting, alongside concerns about system complexity, workflow fit, training and resource implications. Interest-holders emphasised the importance of user-friendly design, interoperability, responsive training, phased implementation and ongoing feedback.
Conclusions:
This pre-implementation qualitative study identified significant process and resource limitations in manual orthopaedic surgical scheduling, but interest-holder support for a well-designed ML-driven system is strong. While participants anticipated potential benefits for scheduling accuracy, throughput and resource allocation, these perceived advantages will require meaningful user engagement, robust training, phased rollout and evaluation in subsequent implementation and outcome studies.