Implementation of an electronic medical record in family practice:a case study
Bibliographic record
Abstract
BACKGROUND: Electronic medical records (EMRs) have the potential to foster a safer, more effective and more efficient healthcare system. However, their implementation in primary care practice remains a challenge. OBJECTIVE: This study aims at exploring factors that have influenced the successful implementation of an EMR system in a family medicine group (FMG) in the Province of Québec, Canada. METHODS: A case study approach was selected to get a deep understanding of the phenomenon in its context. The case was chosen on the basis that it was the first FMG in Québec to implement a full EMR used by all clinicians. Fifteen semi-structured interviews were conducted with key informants. RESULTS: Factors that have influenced the success of the EMR implementation were classified under three broad themes: a project leader who combined the roles of clinical, technology and knowledge champion; an organisation that was open to and supportive of change; and an evidence-based implementation strategy tailored to the local context and adoption pace. CONCLUSIONS: This study underscores the importance of a champion for successful EMR implementation. It proposes a set of roles and characteristics that could be found in a champion as well as other elements for a successful EMR implementation strategy.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".