Diagnostic Imaging Ordering Practices: Physician Perspectives and Implications for Decision Support
Bibliographic record
Abstract
This study explored how referring physicians order diagnostic imaging (DI) services, and possible methods to reduce inappropriate ordering. Telephone interviews were conducted with non-radiologist physicians (general practitioners and specialists). Interview data were analyzed using grounded theory. Both appropriate and inappropriate DI ordering practices emerged as the overarching themes. Specifically, the majority of participants described their top methods of obtaining information support as (1) contacting another physician or (2) consulting the literature. Additionally, participants discussed contributing factors and solutions to inappropriate DI ordering, including clinical decision support systems. These results were used to inform the design of a DI decision support system prototype. This study explored ways to reduce inappropriate DI ordering and identified socio-technical factors that need to be considered when developing ways to mitigate this phenomenon. Promoting more appropriate ordering can improve patient safety and the responsible use of limited diagnostic imaging resources.
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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 | 0.000 | 0.001 |
| 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".