Abstract
The development of a learning health system requires a shift from intermittent retrospective review of outcomes to continuous data-driven understanding of surgical processes and patient outcomes. Doing so will require the routine prospective capture of high-quality structured data. This lack of structured data has been a significant impediment to the development of surgical data science1,2. Laparoscopic cholecystectomy, a high-volume procedure, represents an opportunity to address this, but application of these data will require a paradigm shift in outcome reporting. As part of a systems-wide quality-improvement programme an end-to-end perioperative electronic workflow solution was refined and implemented at Christchurch Hospital, a tertiary referral centre, in 20133. This system allows for end-to-end data capture, including wait listing, operation booking, synoptic operation note documentation, and prospective capture of surgical complications and outcomes. Coupled with this, Christchurch Hospital has undertaken a department-wide initiative to standardize processes in laparoscopic cholecystectomy to optimize outcomes and prevent bile duct injury (BDI)4. Integration of prospective data capture into the clinical workflow allows for the possibility of real-time outcome monitoring and quality improvement.
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