Primary health care teams' experience of electronic medical record use after adoption.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study explored the views and perspectives of primary health care providers participating in the DELPHI (Deliver Primary Healthcare Information) project regarding their experiences using electronic medical records (EMRs) in their practices 2 years after adoption. This research was conducted in follow up to a previous qualitative study looking at early EMR implementation experiences. METHODS: This descriptive qualitative study explored the experiences of 19 participants. Semi-structured interviews were conducted. Both individual and team analyses were performed. RESULTS: Emergent from the data were five interwoven elements of team behavior when using the EMR. Consistent data entry was imperative to successful EMR utilization. The EMR software was utilized differently depending on the role of the team member. Team members continued to seek out a team champion/problem solver to help overcome obstacles. Communication was enhanced by using the common messaging system within the EMR. Finally, success with certain functions such as communication, champion enthusiasm, and recognition of the value of the EMR encouraged others to learn additional features and advanced the adoption process. CONCLUSIONS: These findings illuminate important elements of team behavior that promoted EMR adoption and provide insight for primary health care providers moving through the continuum of initial to advanced EMR adoption.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".