Implementation of an electronic medical record in family practice:a case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it