Enhancing Appropriate Coagulation Testing at a Large Academic Hospital
Bibliographic record
Abstract
Abstract Background: With the advent of Choosing Wisely, there has been a greater emphasis on resource stewardship in medicine. However, broad over-utilization of laboratory tests continues to be a problem, resulting in unnecessary costs and may even result in patient harm. Furthermore, overutilization can lead to a cascade of unnecessary laboratory activities with important direct and indirect costs to an already overburdened system. Coagulation testing provides a prime opportunity to make an impact in the reduction of unnecessary laboratory tests as there are clear indications for testing. Despite being validated for specific clinical circumstances, the PT/aPTT are frequently ordered as screening tests and often ordered together, demonstrating a gap in clinician understanding of the appropriate use of these tests. Educational interventions have previously been considered to be a less effective means of instituting sustainable change in lab test ordering practices. We set out to demonstrate if a multi-modal, multi-disciplinary educational intervention, embedded in a larger quality improvement (QI) initiative could enhance the appropriate use of coagulation testing and minimize laboratory resource waste at St. Michael's Hospital in Toronto, Canada. Here we present on the educational materials that were developed with multiple stakeholder involvement to support the QI process changes. Methods: In this prospective, sequential QI study we developed numerous educational materials and Electronic Medical Records (EMR) decision support prompts to facilitate knowledge translation in coagulation test ordering. The materials were developed with the input of medical students, residents, nursing staff, laboratory technologists, hematology and general internal medicine staff. The materials included educational posters and infographics distributed in clinical work areas and sent monthly via email to nurses, nurse practitioners, laboratory staff and physicians (Figure 1). Medical trainees carry out a large proportion of lab test ordering in our academic hospital, therefore we designed educational materials targeting this cohort. There was also an educational decision support prompt embedded in the EMR order system such that if a clinician attempted to order PT/aPTT testing, a message regarding the appropriate use of these tests appeared (Figure 2). Responding to practitioner requests for additional educational materials, we developed a drug- anticoagulant matching chart as well as posters on appropriate and inappropriate indications for direct oral anticoagulant test ordering. To assess the success of the educational materials, we assessed the volume of calls received by the lab's central receiving regarding coagulation testing. We also qualitatively assessed practitioner level satisfaction and modified the materials based on received feedback. Results: Substantial practitioner level satisfaction with the educational materials was evident and feedback was incorporated through multiple PDSA cycles. Following the implementation of the educational materials, central receiving laboratory technicians felt more empowered to field clinician questions regarding the appropriate use of coagulation testing. Moreover, the volume of calls to the central receiving department regarding PT/aPTT testing progressively decreased from 40-50 calls per day to 3-4 calls per day over three months when the initial multi-modal educational intervention was launched. These changes were sustainable and continue to be seen more than 2 years after the implementation of the educational intervention. This was mirrored by a significant decrease in coagulation testing rates in the hospital as part of the larger QI initiative. Conclusion: Our results indicate that a multi-modal, multi-disciplinary educational initiative that complemented a QI intervention resulted in practitioner satisfaction and support towards improved coagulation test utilization practices at a large academic centre. We believe the educational materials on coagulation testing disseminated to clinicians and embedded in the EMR ordering system effectively enhanced appropriate ordering practices and empowered practitioners as shown by the dramatic reduction in questions pertaining to coagulation testing. We believe that this highlights the importance of education to round out medical QI initiatives. Disclosures Sholzberg: Octapharma: Research Funding; CSL Behring: Research Funding; Amgen: Research Funding; Shire: Research Funding.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".