Electronic medical records and primary care quality: Evidence from Manitoba
Bibliographic record
Abstract
Improvements in quality of care through supporting decision-making processes and increased efficiency have prompted widespread implementation of electronic medical records (EMRs) in Canada. Using a set of indicators of preventive care, chronic disease management, and hospitalizations due to ambulatory care sensitive conditions (ACSC), this study measures the effect of EMR adoption on quality of primary care measures. Population-based data for the Canadian province of Manitoba are used in a difference-in-differences approach with patient- and time-fixed effects. Evidence of changes in the selected quality-of-care indicators is weak, with preventive care, management of asthma, and hospitalizations showing no significant change due to EMR adoption. A statistically significant increase in the quality of diabetes care was found for EMR users, changes being larger for late EMR adopters which is possibly explained by a network effect. This research demonstrates that measuring whether EMRs prompt changes in the quality of care confronts serious challenges. The rapid evolution and gradual adoption of EMR technology, the inevitable learning/acceptance process by individual health practitioners, and its potential reflection on different patient populations create unmeasurable variables that confound EMRs' impact. This study also underscores the importance of data development to support the economic value of EMRs.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".