Use of electronic medical record queries to support clinical decision quality assurance.
Bibliographic record
Abstract
239 Background: Clinical Treatment Decisions in radiation oncology direct the patient’s treatment plans. There is a need for Clinical Decisions to be peer reviewed preferably in real time before the patient plan is completed. Traditional peer review of clinical decisions which ensure high quality patient treatments can be challenging in a busy radiation oncology clinic. Leveraging Electronic Medical Records (EMR) to query standard patient staging and demographics data per disease type allows for efficient peer review of the clinical decision. Methods: Through the use of EMR system (Aria, Varian Medical Systems, Palo Alto CA), data is entered into the radiation oncology chart by the primary radiation oncologist during a patient’s work up. Tools within the EMR have been configured to automatically query patient charts and summarize the data. A second Oncologist runs the query, reviews the data and peer reviews clinical decision for radiotherapy including treatment intent, dose and target contours. The radiation oncologist can then discuss modifications with the original oncologist, or indicate to the dosimetrist to continue planning. Those clinical decisions that are uncertain are escalated to review in a traditional peer review setting. Results: The EMR queries have allowed a shift to real time peer review of clinical decisions. The summary of disease specific staging and demographics data has added to efficiency in clinic in both the oncologists’ ability to complete a timely peer review and the lowering the amount of planning rework. Traditional peer review setting is then used to discuss the controversial and complex cases that would receive the most benefit. Conclusions: The ability to leverage electronic medical record data has made the peer review of clinical decisions in our institution more efficient and therefore the majority can be completed in real time.
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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 | 0.068 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".