How many patients can each surgeon have on their waiting list and still treat them all in time?
Bibliographic record
Abstract
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 patient- and 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 surgeon- and 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".