Reducing Unnecessary Coagulation Testing in Outpatients (REDUCTION): A Quality Improvement Project
Bibliographic record
Abstract
Abstract Introduction: The prothrombin time (PT) and partial thromboplastin time (PTT) are often considered 'routine' tests in the outpatient setting. They are frequently used as screening tests for bleeding disorders in unselected patients despite sensitivities ranging of 1-2 %. For the most common bleeding disorder, a negative score on a bleeding assessment tool (BAT), in the presence of a negative family history, has a NPV of 1. A recent quality improvement study conducted in the emergency department at our institution resulted in a greater than 2-fold reduction in coagulation test ordering rates and more than $6000 CAN direct cost-savings per month. Objectives: We developed a multimodal sequential quality improvement study in the outpatient family practice unit with the primary aim of minimizing unnecessary coagulation testing and maximizing the appropriate use of a BAT. Materials and Methods: We have developed four interventions using plan-do-study-act (PDSA) cycles to achieve our goal. Cycle (1) baseline assessment of ordering rates, involved stakeholder engagement, development of educational tools and addition of the BAT onto the electronic medical record (EMR). Cycle (2) involved assessment of EMR-available BAT. Cycle (3) involved an audit to determine appropriateness of coagulation testing for patients on oral anticoagulants and a subsequent targeted intervention. Cycle (4) involved alteration of the laboratory requisition and facilitating access to decision support and educational materials. Rates were calculated per 100 patient visits per month. Data were analyzed on Strata 13 using an interrupted time series analysis. Results: After Cycle 1, we saw a significant decrease in rates of PT and PTT ordering from 1.9 tests per 100 patient visits per month (for both tests) to 1.5 and 1.4 respectively; (p< 0.001 for both). However, the rate of creatinine (control) testing rate also decreased significantly (8.9 to 8; p=0.006). With Cycle 2, we found that all referrals for bleeding from the family practice unit were appropriate and only 19% of patients who had a BAT completed were referred. In Cycle 3, we identified an inappropriate PT and PTT ordering rate of 11% and 36% respectively for patients on oral anticoagulants. We also identified that PT and PTT were being tested together despite being ordered individually; further investigation revealed that they were being erroneously coupled by laboratory technicians. Finally, results of Cycle 4 showed no significant change in INR (increase by 0.05; p=0.64) or PTT (increase by 0.01; p=0.95) testing rates but a significant increase in control testing rates (0.68; p=0.016) in the first 4 months after the intervention. Conclusions: From our previous quality improvement project in the emergency department, we learned that educational interventions alone are often not sufficient and that a process change is required to achieve meaningful change in coagulation test utilization. Through EMR integration of the BAT we successfully guided hematology referrals for bleeding. We were unable to alter rates of PTT testing despite physically removing the PTT from the lab requisition. This likely reflects the strength of the cultural link between INR and PTT. Planning is underway for PDSA Cycle 5 which will consist of a lab audit of PTT testing and subsequent targeted intervention. Disclosures James: Shire: Research Funding; Bayer: Research Funding; CSL Behring: Research Funding.
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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 | 0.042 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".