A Software System to Collect Expert Relevance Ratings of Medical Record Items for Specific Clinical Tasks
Bibliographic record
Abstract
Development of task-specific electronic medical record (EMR) searches and user interfaces has the potential to improve the efficiency and safety of health care while curbing rising costs. The development of such tools must be data-driven and guided by a strong understanding of practitioner information requirements with respect to specific clinical tasks or scenarios. To acquire this important data, this paper describes a model by which expert practitioners are leveraged to identify which components of the medical record are most relevant to a specific clinical task. We also describe the computer system that was created to efficiently implement this model of data gathering. The system extracts medical record data from the EMR of patients matching a given clinical scenario, de-identifies the data, breaks the data up into separate medical record items (eg, radiology reports, operative notes, laboratory results, etc), presents each individual medical record item to experts under the hypothetical of the given clinical scenario, and records the experts' ratings regarding the relevance of each medical record item to that specific clinical scenario or task. After an iterative process of data collection, these expert relevance ratings can then be pooled and used to design point-of-care EMR searches and user interfaces tailored to the task-specific needs of practitioners.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".