Identification of appropriate patients for preoperative evaluation: A quality improvement project
Bibliographic record
Abstract
Objective: Appropriate preoperative evaluation is essential for safe surgical care. Cost containment and “best practice” suggest that preoperative testing should be matched to patient co-morbidities and the magnitude of the planned procedure. The purpose of this project was to reduce the number of unnecessary referrals to our preoperative evaluation clinic (POE), increase clinic capacity for medically complex patients including diabetics, and quantify the reduction in institutional cost associated with the project.Methods: In addition to other educational activities, a simplified algorithm and optional screening tool were created to assist surgeons with determining which patients should go to POE. A sub-group of pilot surgeons were selected to participate and their POE referral performance was tracked and shared with them. Surgeons were encouraged to send all of their diabetic patients through POE. A cost analysis was carried out to quantify changes in institutional average cost per case for preoperative evaluation, before vs. after project launch. The first quarter of 2013 (pre-project launch) was compared to first quarter 2015.Results: Pilot surgeons reduced referrals to POE by 30%, while decreasing the institutional average cost per case of preoperative evaluation by > 50%. Clinic capacity for complex patients increased, although diabetic referrals remained flat during the project. There was no increase in day of surgery cancellations.Conclusions: This project demonstrates that patterns of preoperative evaluation for healthy patients undergoing low-acuity surgery can be changed, bringing about cost savings, without increasing day of surgery cancellations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".