The current and potential uses of Electronic Medical Record (EMR) data for primary health care performance measurement in the Canadian context: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Electronic Medical Records (EMRs) are a rich data source to measure and improve quality of care. As Canadian primary health care (PHC) EMRs mature, there is increasing potential use of EMR data for performance measurement. This study identifies and describes current uses of EMR data for performance measurement and considerations to further its potential in the Canadian context. METHODS: We applied a qualitative case study design and descriptive assessment in three phases, consulting multiple data sources including scientific and grey literature, system leaders (n = 41), and clinician/researchers (n = 20). Phases included a multimethod approach to identify initiatives using EMR data for performance measurement across Canadian jurisdictions; in-depth review of current initiatives identified from a healthcare performance intelligence lens; and triangulation and thematic analysis across data sources to explore considerations for advancing performance measurement uses of EMR data in the Canadian context. RESULTS: Six initiatives of EMR data use for performance measurement were identified: one multi-jurisdictional; five jurisdiction-specific in the provinces of British Columbia, Manitoba and Ontario. EMR data uses were predominately for micro-level PHC physician and team performance improvement, with some use for meso-level organization/network-wide improvement. Indicator sets varied in number, though shared emphasis on chronic disease management and prevention/screening and to a lesser extent medication management. Key considerations for governing, resourcing and implementing EMR data for performance measurement were identified. CONCLUSIONS: The extent of EMR data use for performance measurement varies across Canada. To further its potential, pan-Canadian data and privacy standards, performance intelligence competencies and renewed core PHC indicators should be prioritized. Experiences across countries, coupled with increasing momentum for performance measurement using real-world data, should be leveraged to avoid unnecessarily slow progress in Canada and abroad.
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How this classification was reachedexpand
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 | 0.038 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".