Impact of electronic medical record on physician practice in office settings: a systematic review
Bibliographic record
Abstract
Background: Increased investments are being made for electronic medical records (EMRs) in Canada. There is a need to learn from earlier EMR studies on their impact on physician practice in office settings. To address this need, we conducted a systematic review to examine the impact of EMRs in the physician office, factors that influenced their success, and the lessons learned. \nResults: For this review we included publications cited in Medline and CINAHL between 2000 and 2009 on \nphysician office EMRs. Studies were included if they evaluated the impact of EMR on physician practice in office \nsettings. The Clinical Adoption Framework provided a conceptual scheme to make sense of the findings and allow \nfor future comparison/alignment to other Canadian eHealth initiatives. \nIn the final selection, we included 27 controlled and 16 descriptive studies. We examined six areas: prescribing \nsupport, disease management, clinical documentation, work practice, preventive care, and patient-physician \ninteraction. Overall, 22/43 studies (51.2%) and 50/109 individual measures (45.9%) showed positive impacts, 18.6% \nstudies and 18.3% measures had negative impacts, while the remaining had no effect. Forty-eight distinct factors \nwere identified that influenced EMR success. Several lessons learned were repeated across studies: (a) having \nrobust EMR features that support clinical use; (b) redesigning EMR-supported work practices for optimal fit; (c) \ndemonstrating value for money; (d) having realistic expectations on implementation; and (e) engaging patients in \nthe process. \nConclusions: Currently there is limited positive EMR impact in the physician office. To improve EMR success one \nneeds to draw on the lessons from previous studies such as those in this review.
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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 | 0.026 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".