How do experts determine where to intervene on test ordering? An interview study
Bibliographic record
Abstract
OBJECTIVES: Lab testing is a high-volume activity that is often overused, leading to wasted resources and inappropriate care. Improving test ordering practices in tertiary care involves deciding where to focus scarce intervention resources, but clear guidance on how to optimize these resources is lacking. We aimed to explore context-sensitive factors and processes that inform individual decisions about laboratory stewardship interventions by speaking to key interest holders in this area. METHODS: We conducted semi-structured interviews with test-ordering intervention development experts and authors of test-ordering guidance documents to explore five broad topics: 1) processes used to prioritize tests for intervention; 2) factors considered when deciding which tests to target; 3) measurement of these factors; 4) interventions selected; 5) suggestions for a framework to support these decisions. Transcripts were double coded using directed-content and thematic analysis. RESULTS: We interviewed 14 intervention development experts. Experts noted they frequently consider test volume, test value, and patient care when deciding on a test to target. Experts indicated that quantifying many relevant factors was challenging. Processes to support these decisions often involved examining local data, obtaining buy-in, and relying on an existing guideline. Suggestions for building a framework emphasized the importance of collaboration, consideration of context and resources, and starting with "easy wins" to gain support and experience. CONCLUSIONS: Our study provides insight into the factors and processes experts consider when deciding which tests to target for intervention and can inform the development of a framework to guide the selection of tests for intervention and guideline development.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".