Reducing use of coagulation tests in a family medicine practice setting: An implementation study
Bibliographic record
Abstract
Introduction Clinicians often order the international normalized ratio (INR) and activated partial thromboplastin time (APTT) to evaluate for the possibility of inherited bleeding disorders despite sensitivities and specificities of 1%–2%. The most accurate tool to evaluate for bleeding disorders is a validated bleeding assessment tool (BAT). Our aim was to reduce coagulation testing by >50% in a large family practice in Ontario, Canada. Methods We conducted an implementation study from May 2016 to February 2020. Iterative interventions included introduction of a validated BAT into the electronic medical record (EMR); removal of the APTT as a prepopulated selection from the laboratory requisition; and education targeting family medicine teams and laboratory personnel. The primary outcome was the rate of pre‐ and post‐APTT testing. Creatinine testing was the control. Data were analyzed via an interrupted time series analysis using Stata 13. Results Immediately following education of the laboratory personnel on coagulation testing, the APTT rate level dropped by 1.26 tests per 100 patient visits per month ( p < 0.001) and was sustained until the end of the study. Meanwhile, the PT/INR and creatinine testing rate levels did not change (rate level = −0.02 per 100 visits per month, p = 0.79 and 0.49, p = 0.22 respectively). There was good uptake of the BAT following integration and 18/88 (20%) obtained a referral to hematology after BAT completion. Conclusions Multidisciplinary, iterative interventions reduced APTT testing and enabled the use of BATs to guide hematology referrals in a large family practice.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".