Self-reported test ordering practices among Canadian internal medicine physicians and trainees: a multicenter cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Over-testing is a recognized problem, but clinicians usually lack information about their personal test ordering volumes. In the absence of data, clinicians rely on self-perception to inform their test ordering practices. In this study we explore clinician self-perception of diagnostic test ordering intensity. METHODS: We conducted a cross-sectional survey of inpatient General Internal Medicine (GIM) attending physicians and trainees at three Canadian teaching hospitals. We collected information about: self-reported test ordering intensity, perception of colleagues test ordering intensity, and importance of clinical utility, patient comfort, and cost when ordering tests. We compared responses of clinicians who self-identified as high vs low utilizers of diagnostic tests, and attending physicians vs trainees. RESULTS: Only 15% of inpatient GIM clinicians self-identified as high utilizers of diagnostic tests, while 73% felt that GIM clinicians in aggregate ("others") order too many tests. Survey respondents identified clinical utility as important when choosing to order tests (selected by 94%), followed by patient comfort (48%) and cost (23%). Self-identified low/average utilizers of diagnostic tests were more likely to report considering cost compared to high utilizers (27% vs 5%, P = 0.04). Attending physicians were more likely to consider patient comfort (70% vs 41%, p = 0.01) and cost (42% vs 17%, p = 0.003) than trainees. CONCLUSIONS: In the absence of data, providers seem to recognize that over investigation is a problem, but few self-identify as being high test utilizers. Moreover, a significant percentage of respondents did not consider cost or patient discomfort when ordering tests. Our findings highlight challenges in reducing over-testing in the current era.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".