The Contribution of Office-Based EMR Systems to the Performance of Family Physicians and Primary Care Medical Practices
Bibliographic record
Abstract
In this study we sought to better understand how EMR systems are actually being used by family physicians and what they perceive to be the performance outcomes for themselves and their medical practices. To achieve our objectives, we conducted a survey of family physicians in Quebec, Canada and obtained responses from 331 user physicians. Key findings reveal that EMR systems "as-used" vary from one physician to another in terms of the EMR capabilities that are actually mobilized by them. Two user profiles were identified, that is, Meaningful and Basic users. Significant differences between the two groups were found in terms of physician demographics and system characteristics. In terms of perceived outcomes, physicians were clustered under three profiles that could be clearly distinguished from one another, namely Highly Impacted, Slightly Impacted and Non Impacted users. Findings show that Highly Impacted physicians are those who are the most experienced with EMRs and those who make the most wide-ranging use of their system capabilities. Practical and research implications of this study are discussed.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".