Implementation of electronic medical records: theory-informed qualitative study.
Bibliographic record
Abstract
OBJECTIVE: To apply the diffusion-of-innovations theory to the examination of factors that are perceived by family physicians as influencing the implementation of electronic medical records (EMRs). DESIGN: Qualitative study with 2 focus groups 18 months after EMR implementation; participants also took part in a concurrent quantitative study examining EMR implementation and preventive services. SETTING: Toronto, Ont. PARTICIPANTS: Twelve community-based family physicians. METHODS: We employed a semistructured interview guide. The interviews were audiotaped and transcribed verbatim; 2 researchers independently categorized and coded the transcripts and then met to compare and contrast their findings, category mapping, and interpretations. Findings were then mapped to an existing theoretical framework. MAIN FINDINGS: Multiple barriers to EMR implementation were described. These included lack of relative advantage for many processes, high complexity of the system, low compatibility with physician needs and past experiences, difficulty with adaptation of the EMR to the organization and adaptation of the organization to the EMR, and lack of organizational slack. Positive factors were the presence of a champion and relative advantages for some processes. CONCLUSION: Early EMR implementation experience is consistent with theoretical concepts associated with implementation of innovations. A problematic implementation process helps to explain, at least in part, the lack of improvement in preventive services in our quantitative results.
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.
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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".