Exploration of a quality improvement process to standardised preoperative tests for a surgical procedure to reduce waste
Bibliographic record
Abstract
BACKGROUND: Preoperative tests are done to determine a patient's fitness for anaesthesia and surgery. LOCAL PROBLEM: Although routine tests before surgery in the absence of specific clinical indications are not recommended, we observed high volumes of routine preoperative tests were performed in our institution. We describe a process to implement a standardised preoperative investigational approach to reduce unnecessary testing before surgeries. METHODS: A series of six Plan-Do-Study-Act (PDSA) cycles was conducted for root cause analysis and process mapping, development of standardised tool (GRID), collection of baseline data, education and feedback, pilot testing and implementation and uptake of GRID.Root cause analysis revealed a lack of awareness of guidelines and a lack of a standardised tool to guide preoperative testing. We undertook a pilot quality improvement project to reduce unnecessary testing before knee and hip arthroplasty by developing and implementing a standardised tool (GRID) and engaging all stakeholders. INTERVENTIONS: A clinical development team (CDT) was formed, including all the stakeholders. Our CDT focused on a continuous rapid cycle improvement strategy. RESULTS: After implementation of the tool in a subgroup of patients undergoing elective hip or knee arthroplasty, unnecessary coagulation tests (activated partial thromboplastin time and the international normalised ratio), electrolyte/renal panel tests and electrocardiograms were reduced by 81% (91%-17%), 81% (41%-7%) and 68% (35%-11%), respectively. No surgery was delayed or cancelled due to tests not performed before surgery. CONCLUSIONS: A standardised preoperative investigational approach based on patients' medical conditions rather than routine testing can reduce unnecessary tests before surgery. Further, implementing guidelines is more complex than developing guidelines. Hence, continuous PDSA cycles are essential to evaluate the processes in a quality improvement project. It can take time to build teams and have shared goals; however, once this is achieved, the success of a quality improvement project is certain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".