Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Increasing utilization of electronic medical records (EMRs) presents an opportunity to efficiently measure quality indicators in primary care. Achieving this goal requires the development of accurate patient-disease registries. This study aimed to develop and validate an algorithm for identifying patients with ischemic heart disease (IHD) within the EMR. METHODS: An algorithm was developed to search the unstructured text within the medical history fields in the EMR for IHD-related terminology. This algorithm was applied to a 5% random sample of adult patient charts (n = 969) drawn from a convenience sample of 17 Ontario family physicians. The accuracy of the algorithm for identifying patients with IHD was compared to the results of 3 trained chart abstractors. RESULTS: The manual chart abstraction identified 87 patients with IHD in the random sample (prevalence = 8.98%). The accuracy of the algorithm for identifying patients with IHD was as follows: sensitivity = 72.4% (95% confidence interval [CI]: 61.8-81.5); specificity = 99.3% (95% CI: 98.5-99.8); positive predictive value = 91.3% (95% CI: 82.0-96.7); negative predictive value = 97.3 (95% CI: 96.1-98.3); and kappa = 0.79 (95% CI: 0.72-0.86). CONCLUSIONS: Patients with IHD can be accurately identified by applying a search algorithm for the medical history fields in the EMR of primary care providers who were not using standardized approaches to code diagnoses. The accuracy compares favorably to other methods for identifying patients with IHD. The results of this study may aid policy makers, researchers, and clinicians to develop registries and to examine quality indicators for IHD in primary care.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| 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 it