Towards a Knowledge-Based Recommender System for Linking Electronic Patient Records With Continuing Medical Education Information at the Point of Care
Bibliographic record
Abstract
Given the limits of human memory, clinicians have trouble recalling therapeutic recommendations, even when the clinician previously judged that the information relevant for the care of a specific patient. To tackle this problem, we present a knowledge-based recommender system prototype that links the electronic patient records to clinical information, previously delivered to the target physician and judged to be potentially beneficial. We developed this prototype within the context of RxTx, a Canadian continuing medical education program. We apply a constraint-based recommendation strategy as follows: (1) clinical experts (taggers) map a set of therapeutic recommendations (called Highlights) to a requirement statement built from the standard clinical codes and supplementary demographic information, when applicable; (2) a matching system identifies patient-Highlights recommendation pairs through requirement satisfaction; and (3) given a patient record being examined, the recommended Highlights can be retrieved online at the point of care. We tested this prototype using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network and 87 therapeutic Highlights from the RxTx collection, evaluating the system's performance against a gold standard consisting of a two-expert consolidated patient-Highlight matching set for 150 patient records. The requirements-based recommendation system exhibits very high precision (mode: 1.0, 89% of the time; average precision: 0.95) and moderate recall (mode 1.0, 48.7% of the time; average recall: 0.61). The near-perfect precision minimizes the possibility of generating alert fatigue in physicians using the system. We note that more than half of the false negative results from the information being available in the text of the electronic medical records, but unavailable as a clinical code. The near-perfect precision over the tested patient set suggests that the system has the potential to deliver high-quality recommendations of clinical information at the point of care while being easily integrated within a continuing medical education program and the clinician's workflow.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".