Developing and implementing linked electronic medical record and administrative data in primary care practice for diabetes in Alberta
Bibliographic record
Abstract
IntroductionUse of administrative health data and primary care electronic medical record data are both ubiquitous in Alberta, but linkage between them at patient level and implementation of the linked data into primary care practice are rare. This demonstration project sought to achieve this for a sample of patients with diabetes. Objectives and ApproachAcademic family physicians in the Department of Family Medicine at the University of Calgary who participate in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) identified diabetes–related variables, either in their EMRs or in administrative data, that they wished to obtain in a linked dataset. Secure data linkage was obtained through Alberta Health Services (the provincial health authority) following transmission of patient mapping files direct from the clinics. The de-identified, linked, patient data was then transferred to CPCSSN-Alberta data managers for processing and displayed to users through an interactive Diabetes Dashboard. Results2598 patients with diabetes were identified using a validated CPCSSN case definition from 47 family physicians in three clinics. CPCSSN EMR data included primary care encounters, date of diagnosis, deprivation index, BMI, blood pressure, comorbidity, diabetes medications prescribed, risk factors, etc. Administrative data included laboratory results (HbA1c, fasting blood glucose, cholesterol, triglycerides, creatinine), medication dispensed, emergency room visits, inpatient admissions and costs. Integrated, interactive provider reports were created and sent to participating physicians. The reports presented the information about diabetes patients at individual provider level, bench-marked at clinic, primary care network and provincial levels. Follow-up with providers led to further dashboard development . We propose to scale up implementation of the integrated diabetes database and dashboard to include all 23,000 CPCSSN-identified diabetes patients in Alberta. Conclusion/ImplicationsIntegration of EMR and administrative data and its application to clinical care, panel management, and quality improvement in primary care, as well as to surveillance and research, was feasible and acceptable to the family physicians participating in this project.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".