Surgical scheduling can be a complicated and timely process that takes into account a variety of factors, including the availability of operating rooms (ORs), staffing and pre- and post-operative care resources. Optimizing scheduling and the use of OR time is crucial to enhance surgical efficiency, patient flow and the overall patient experience.

The Hospital for Sick Children (SickKids) has been making improvements to its surgical booking processes to help reduce the hospital’s surgical waitlist, which, like other hospitals, has been severely impacted by the COVID-19 pandemic. As part of these efforts, SickKids sought out a graphic user interface (GUI) to leverage its new machine learning surgical schedule model in the hopes of effectively assessing, predicting and optimizing case bookings in order to maximize available OR time, inpatient beds, and post-operative care resources.

Orchid Analytics, an operations research company that builds custom simulations and statistical models to help hospitals and clinics make decisions, will work to customize their surgical scheduling platform (GUI) and connect it to SickKids’ machine learning model. The project team’s goals are to facilitate informed, data-driven decisions with regards to:

• OR block allocation,
• surgeon scheduling,
• post-operative destination availability, and
• other factors pertaining to OR smoothing, throughput, resourcing, and patient flow.

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Technology icon
Type of Technology
Graphic user interface
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Project Year
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