Evaluation of Electronic Medical Record Administrative data Linked Database (EMRALD).
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
BACKGROUND: Primary care electronic medical records (EMRs) represent a potentially rich source of information for research and evaluation. OBJECTIVE: To assess the completeness of primary care EMR data compared with administrative data. STUDY DESIGN: Retrospective comparison of provincial health-related administrative databases and patient records for more than 50,000 patients of 54 physicians in 15 geographically distinct clinics in Ontario, Canada, contained in the Electronic Medical Record Administrative data Linked Database (EMRALD). METHODS: Physician billings, laboratory tests, medications, specialist consultation letters, and hospital discharges captured in EMRALD were compared with health-related administrative data in a universal access healthcare system. RESULTS: The mean (standard deviation [SD]) percentage of clinic primary care outpatient visits captured in EMRALD compared with administrative data was 94.4% (4.88%). Consultation letters from specialists for first consultations and for hospital discharges were captured at a mean (SD) rate of 72.7% (7.98%) and 58.5% (15.24%), respectively, within 30 days of the occurrence. The mean (SD) capture within EMRALD of the most common laboratory tests billed and the most common drugs dispensed was 67.3% (21.46%) and 68.2% (8.32%), respectively, for all clinics. CONCLUSIONS: We found reasonable capture of information within the EMR compared with administrative data, with the advantage in the EMR of having actual laboratory results, prescriptions for patients of all ages, and detailed clinical information. However, the combination of complete EMR records and administrative data is needed to provide a full comprehensive picture of patient health histories and processes, and outcomes of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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