Standardizing Multidisciplinary Case Conferences and Improving Communication Between Referring Physicians and Radiologists: A Quality Improvement Initiative
Bibliographic record
Abstract
Purpose: Assess the effectiveness of standardizing multidisciplinary case conferences (MDCs). Methods: Anonymous electronic surveys gauged opinions of abdominal radiologists engaged in recurring MDCs. A standardized Excel template, following Cancer Care Ontario guidelines and relevant literature, was distributed to MDC managers. Physicians were instructed to send cases 36 hours prior to MDC. Template adherence was assessed at 1.5 and 8 months. A follow-up survey at 4 months evaluated the intervention’s effectiveness. Results: 27/34 abdominal radiologists provided 47 baseline responses, and 12 delegated radiologists provided 23 follow-up responses. “Often/always” being provided the image’s location increased from 36% (17/47) at baseline to 70% (16/23) at follow-up. Non-adherence to the 36-hour cut-off decreased from 36% (16/45) to 17% (4/23). 72% disagreed that uploading remote imaging to hospital servers is easy (33/46), similar to follow-up (18/23, 78%). In assessing the intervention, 41% noted improved standardization (9/22), another 41% considered MDCs already standardized (9/22), and 18% reported no change (4/22). Those reporting no change experienced a higher frequency of non-adherence to the 36-hour cut-off (3/4, 75%) than others (1/18, 6%), and less frequent “often/always” ratings for image location being provided (3/4, 75%) than others (2/18, 11%). 89% (25/28) of MDCs adhered to the template. Issues regarding last-minute add-on cases may be mitigated through EPIC force functions. Artificial intelligence advancements may assist in retrieving external images and patient information. Conclusion: Adherence to MDC standardization was high, allowing for more efficient preparation, potentially reducing radiologist administrative burdens. Future force functions and artificial intelligence integration into electronic patient records may further augment this.
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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 | 0.048 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".