Implementing electronic health records: Key factors in primary care.
Bibliographic record
Abstract
OBJECTIVE: To examine common themes about implementing and adopting electronic health record (EHR) systems that emerged from 3 separate studies of the experiences of primary health care providers and those who implement EHRs. DESIGN: Synthesis of the findings of 3 qualitative studies. SETTING: Primary health care practices in southwestern Ontario and the Centre for Studies in Family Medicine at The University of Western Ontario in London. PARTICIPANTS: Family physicians, other primary health care providers, and the Deliver Primary Healthcare Information management and operations team. METHOD: The findings of 3 separate qualitative studies exploring the implementation of EHRs were synthesized. In the 3 studies, investigators used semistructured interview guides to conduct one-on-one interviews and a focus group, which were audiotaped and transcribed verbatim, to collect information about participants' experiences implementing and adopting EHRs. Transcripts were coded and analyzed by 1 or 2 investigators, and the research team met regularly for synthesis and interpretation of themes. MAIN FINDINGS: Four common themes arose from the 3 studies: expectations of EHRs, time and training required to implement and adopt the software, the emergence of an EHR champion or problem solver, and the readiness of health care providers to accept the system. CONCLUSION: Those considering implementing and adopting EHRs into a family practice environment should reflect on the following issues: their expectations of the system and what is needed to use the software, the level of commitment to EHR implementation and adoption, the availability of someone willing to take a leadership or champion role, and how much knowledge of computers potential EHR users have.
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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 | 0.017 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".