Primary care physicians' experiences with electronic medical records: implementation experience in community, urban, hospital, and academic family medicine.
Bibliographic record
Abstract
OBJECTIVE: To understand how remuneration and care setting affect the implementation of electronic medical records (EMRs). DESIGN: Semistructured interviews were used to illicit descriptions from community-based family physicians (paid on a fee-for-service basis) and from urban, hospital, and academic family physicians (remunerated via alternative payment models or sessional pay for activities pertaining to EMR implementation). SETTING: Small suburban community and large urban-, hospital-, and academic-based family medicine clinics in Alberta. All participants were supported by a jurisdictional EMR certification funding mechanism. PARTICIPANTS: Physicians who practised in 1 or a combination of the above settings and had experience implementing and using EMRs. METHODS: Purposive and maximum variation sampling was used to obtain descriptive data from key informants through individually conducted semistructured interviews. The interview guide, which was developed from key findings of our previous literature review, was used in a previous study of community-based family physicians on this same topic. Field notes were analyzed to generate themes through a comparative immersion approach. MAIN FINDINGS: Physicians in urban, hospital, and academic settings leverage professional working relationships to investigate EMRs, a resource not available to community physicians. Physicians in urban, hospital, and academic settings work in larger interdisciplinary teams with a greater need for interdisciplinary care coordination, EMR training, and technical support. These practices were able to support the cost of project management or technical support resources. These physicians followed a planned system rollout approach compared with community physicians who installed their systems quickly and required users to transition to the new system immediately. Electronic medical records did not increase, or decrease, patient throughput. Physicians developed ways of including patients in the note-taking process. CONCLUSION: We studied physicians' procurement approaches under various payment models. Our findings do not suggest that one remuneration approach supports EMR adoption any more than another. Rather, this study suggests that stronger physician professional networks used in information gathering, more complete training, and in-house technical support might be more influential than remuneration in facilitating the EMR adoption experience.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".