A-176 Barriers and Enablers to Laboratory Stewardship Across a 16 Hospital Regional System
Bibliographic record
Abstract
Abstract Background Evidence indicates substantial inappropriate and inefficient use of resources in laboratory medicine. Commonly-used approaches that can be effective in improving practice include education, changes to processes or systems, and audit & feedback. The overall aims of this project were to survey the experiences of individuals implementing laboratory stewardship activities across a regional lab system, focusing on identifying key enablers and barrier to success. Methods We used a mixed methods approach beginning with a questionnaire to gather brief details about implementation efforts (what was done, how interventions were implemented, when, and by whom). These preliminary findings served as a precursor to conducting focus groups with implementation teams at each of the sites. We probed the implementation process, drivers of success, and barriers faced. Focus group transcripts were analyzed using thematic analysis. Results Forty individuals took part in the surveys and focus groups. Results revealed a range of progress with lab stewardship initiatives across sites, with a variety of initiatives having been implemented. A wide range of factors impacting progress with laboratory stewardship, both negatively and positively, were discussed. These were grouped into four broad topics: 1) Roles, responsibilities, and relationships—the “Who”; 2) Generating interest in stewardship—the “Why”; 3) Implementation strategies and processes—the “What”; 4) Project management and resources—the “How”. Key barriers identified included: lack of a clear vision which reduces relative priority, lack of resources, emphasis on financial outcomes, and that over-use problems have complex causes. Key enablers identified included active participation of hospital clinical and administrative champions, ability to implement system level changes (e.g., changing electronic orders and processes), focusing on patient safety outcomes, using established change methodologies which support systematic identification of root causes to help select appropriate change strategies as well as formal evaluation. Conclusion While some sites progressed with stewardship initiatives with relative ease, most faced challenges along the way. Surfacing these challenges supports the development of strategies to help address them such that lab stewardship activities can have maximal impact in improving quality of care. Structured and systematic approaches to laboratory stewardship are more likely to be successful than those without designed root cause analysis with targeted interventions and clear outcome metrics.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".