Adoption and use of stand-alone electronic prescribing in a health plan-sponsored initiative.
Bibliographic record
Abstract
OBJECTIVES: To quantify rates of stand-alone e-prescribing (SEP) adoption and use among primary care physicians (PCPs) participating in a SEP initiative and to determine which physician and patient characteristics were associated with higher rates of each. STUDY DESIGN: Using records from an insurer-led SEP initiative, we compared the characteristics of 297 PCPs who adopted SEP through the initiative with the characteristics of 1892 eligible PCPs who did not. Among 297 adopters, we studied the extent of SEP use. METHODS: Dependent variables included each physician's adoption of SEP and his or her e-prescribing use ratio (the ratio of electronic prescriptions to pharmacy claims in the same period). Independent variables included characteristics of PCPs (specialty, practice size, and prescribing volume) and their patients (patient age, sex, race/ethnicity, and household income). RESULTS: Solo practitioners, pediatricians, and physicians with more patients from predominantly African American zip codes were underrepresented among SEP adopters. The mean (SD) e-prescribing use ratio among adopters was 0.23 (0.28). Twenty percent of physicians maintained e-prescribing use ratios above 0.50. Available physician characteristics explained little of the variance in use, but physicians in smaller practices had greater use (P = .02). CONCLUSIONS: Certain categories of physicians may need more tailored incentives to adopt SEP. On average, adopters used the SEP system for only about one-quarter of their prescriptions. Some adopters achieved high levels of SEP use, and further research is needed to elucidate the factors that enabled this.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".