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Improved Surgical Scheduling Software

24 months
Approved budget:
Mr Thomas Adams
Associate Professor Michael O'Sullivan
Dr Cameron Walker
Health issue:
Other (generic health or health services)
Proposal type:
Precision Driven Health Postdoctoral Fellowship
Lay summary
The aim is to develop software that schedules elective surgical sessions quickly and in a way that reduces the chances of the sessions running overtime. The software would use novel machine learning techniques that incorporate historical surgery data to estimate the probability that sessions run overtime. Further research into improved prediction of surgery durations, for use in scheduling sessions, that utilises individual patient data would also be performed.