Predicting the use of electronic prescribing among early adopters in primary care.
Bibliographic record
Abstract
OBJECTIVE: To identify the factors that can predict physicians' use of electronic prescribing. DESIGN: All primary care physicians who practised in a single geographic region in Quebec were invited to use a free, advanced, research-based electronic prescribing and drug management system. This natural experiment was studied with an expansion of the Technology Acceptance Model (TAM), which was used to explain early adopters' use of this electronic prescribing technology. SETTING: Quebec city region. PARTICIPANTS: A total of 61 primary care physicians who practised in a single geographic region where there was no electronic prescribing. MAIN OUTCOME MEASURES: Actual use of electronic prescribing; physicians' perceptions of and intentions to use electronic prescribing; physician and practice characteristics. RESULTS: During the 9-month study period, 61 primary care physicians located in 26 practice sites used electronic prescribing to write 15 160 electronic prescriptions for 18 604 patients. Physician electronic prescribing rates varied considerably, from a low of 0 to a high of 75 per 100 patient visits, with a mean utilization rate of 30 per 100 patient visits. Overall, 34% of the variance in the use of electronic prescribing was explained by the expanded TAM. Computer experience (P=.001), physicians' information-acquisition style (P=.01), and mean medication use in the practice (P=.02) were significant predictors. Other TAM factors that generally predict new technology adoption (eg, intention to use, perceived ease of use, and perceived usefulness) were not predictive in this study. CONCLUSION: The adoption of electronic prescribing was a highly challenging task, even among early adopters. The insight that this pilot study provides into the determinants of the adoption of electronic prescribing suggests that novel physician-related factors (eg, information-acquisition style) and practice-related variables (eg, prevalence of medication use) influence the adoption of electronic prescribing.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".