Adoption of electronic medical records in family practice: the providers' perspective.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The study's objectives were to explore Deliver Primary Healthcare Information (DELPHI) project participants' experiences, ideas, and perspectives regarding the adoption of electronic medical records (EMRs) into their primary health care practices and to examine perceived barriers and facilitators to EMR adoption. METHODS: This study explored the experiences of the 30 participants in the project. Semi-structured interviews were conducted. The analysis was both iterative and interpretive. RESULTS: Two key themes emerged: (1) barriers (ie, level of computer literacy, training required, and time) and facilitators (ie, having an in-house problem solver and the EMR's integrated messaging system), and (2) a continuum of EMR adoption (ie, levels of knowledge ranging from novice to advanced and responses to the EMR that included participants' reflections on their personal journey across the adoption continuum and that of their practice sites). CONCLUSIONS: It is important to be aware of and responsive to factors that can influence EMR implementation and adoption. They include paying attention to computer literacy; setting aside dedicated time for EMR implementation and adoption, as well as engaging in training activities; and supporting problem-solvers in the practice. Mechanisms should be put into place to promote the movement of practices across the continuum of 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".