Exploring the impact of diagnostic imaging decision support embedded in an electronic referral solution on the appropriate ordering of magnetic resonance imaging for patients with knee pain: a retrospective chart review
Bibliographic record
Abstract
RATIONAL AND OBJECTIVE: Requests for magnetic resonance imaging (MRI) exams have notably increased in Canada. However, many of these exams may not always be indicated. The Joint Department of Medical Imaging and the eReferral Program have worked collaboratively to embed an integrated clinical decision support (DS) tool within the eReferral process for diagnostic imaging requests. This retrospective chart review aimed to assess the necessity of MRI exams for knee pain patients at the point of referral in relation to the referral method (no DS tools within fax- vs. DS tools within eReferral). METHODS: Seven hundred and seventeen medical charts of routine MRI referral requests to an Ontario Hospital for patients with knee complaints were reviewed during the study period. The necessity of the MRI exams was evaluated using the supporting algorithm and knee pathway appropriateness guidelines. MRI exams were considered necessary if requested for symptoms or signs that align with best-practice standards, complemented with sound clinical assessment or history of a radiography scan before ordering an MRI. RESULTS: In general, MRI requests made through eReferral were 13.289 times more likely to be necessary orders than those made through fax. The likelihood of referring patients for a necessary MRI exam was higher for eReferral than fax for the year 2018/2019 (53.0% vs. 26.8%, P < 0.001) and for the year 2019/2020 (58.5% vs. 16.3%, P < 0.001). In addition, the rate of ordering X-ray as the proper initial imaging scan for patients presenting with knee pain has steadily increased by 10% over the year for users of the eReferral platform compared to a decrease of 7% for those using fax. CONCLUSION: Our findings highlight the positive impact of integrating DS tools at the point of referral in supporting the ordering of necessary MRI scans, suggesting that service re-design and implementation of automated assistive technology services would impact patient care.
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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 | 0.026 | 0.180 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".