Perspectives on Electronic Medical Record Implementation after Two Years of Use in Primary Health Care Practice
Bibliographic record
Abstract
PURPOSE: This qualitative study explored the experiences of primary health care providers and staff who had moved beyond the stage of implementing electronic medical records (EMRs) in their practices to using this technology on an on-going basis. METHODS: A descriptive qualitative approach was used. Semistructured interviews were conducted with 19 participants. Data analysis was iterative and interpretive. RESULTS: Factors that hindered and motivated ongoing EMR use emerged. Factors that hindered use included (1) information technology challenges such as learning to use the EMR and the computer, electronic connectivity, and scanning; and (2) variability in on-going EMR use. Two factors motivated ongoing use: (1) improved efficiency in patient care, and (2) confidence with computers and EMR software. CONCLUSIONS: Different issues in the use of EMRs surface as primary health care providers and staff mature in their use of this technology. Ongoing use of the EMR may be facilitated by confidence with the technology as well as providers' perceptions of efficiency in patient care. Optimal use of the EMR could be facilitated through assessing and enhancing computer skills, working toward consistent data entry and use of the EMR, and developing strategies to address issues such as scanning and electronic connectivity.
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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 | 0.033 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".