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Objective: In many advanced economies patients wait on elective surgery waiting lists longer than clinically recommended times. This results from either a demand and capacity differential or challenges with the chronological management of patient bookings. This paper describes a novel algorithm that calculates elective surgery capacity and demand imbalances at a surgeon and urgency category level. Methods: An algorithm was developed that is surgeon-specific, sensitive to clinical urgency, relates to patientand procedure level, and is scalable, dynamic and efficient. The novel measure designated the “Nominal Waiting List Maximum”, uses historic waiting list removal rates to approximate waiting list capacity at a surgeonand urgency category-level. This measure can then be compared to the actual patients on each surgeon’s waiting list for each urgency category at a given point in time to measure imbalances. Results: In 2014, the algorithm was automated and implemented across a large Hospital and Health Service (HHS), in QLD, Australia, within an analytics solution. The solution extracts current and historic elective surgery waiting list episode-level data from underlying repositories and calculates “Nominal Waiting List Maximum” for every surgeon at an urgency category level with daily data flows. Conclusions: The solution helped the large tertiary hospital group to identify demand and capacity imbalances at a surgeon and urgency category level to improve theatre session allocations. With the aid of this measure, the HHS achieved zero patients waiting longer than clinically recommended times and was able to hold this position for more than 2 years demonstrating the value of this algorithm. The solution was subsequently rolled out to 55 hospitals across QLD, Australia and anonymised views provided to the hospitals’ governing body.

How many patients can each surgeon have on their waiting list and still treat them all in time?