Using Digital Health to Support Best Practices: Impact of MRI Ordering Guidelines Embedded Within an Electronic Referral Solution
Bibliographic record
Abstract
BACKGROUND: Between 2003 and 2012, the number of MRIs performed in Canada more than doubled to 1.7 million [1]. According to a 2010 Health Council of Canada report nearly 30% of MRIs were inappropriately ordered [2]. The use of diagnostic imaging referral guidelines has been shown to improve the appropriateness of imaging orders [3, 4]. OBJECTIVES: To identify the number of unnecessary pre-consult MRIs ordered for patients with knee pain. As well, the impact that new evidence-based clinical decision support (DS) guidelines embedded within the referral form has had on the number of unnecessary MRIs was investigated. METHODS: This study employed a retrospective design approach. Charts of all knee pain patients over the age of 55 who were referred for consultation to the 5 participating orthopedic surgeons during the study period were reviewed by three medical students. RESULTS: 270 patient charts were included in this study. MRI was ordered for 60 patients with only 56.7% having had a prior X-ray. Of the 60 ordered MRIs, 50 (84%) were considered inappropriate, while only 10 (16%) were appropriate. Our results were compared to previous results of a quality improvement study implemented at the same clinic. A substantial reduction of 12% in the number of pre-consult MRIs and a 5% increase in the number of ordered X-rays before consultation was demonstrated. CONCLUSION: This work highlights the impact of including DS tools within an electronic referral form to support clinical best practices.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".