Even when physicians adopt e-prescribing, use of advanced features lags.
Bibliographic record
Abstract
Physician practice adoption of electronic prescribing has not guaranteed that individual physicians will routinely use the technology, particularly the more advanced features the federal government is promoting with financial incentives, according to a new national study from the Center for Studying Health System Change (HSC). Slightly more than two in five physicians providing office-based ambulatory care reported that information technology (IT) was available in their practice to write prescriptions in 2008, the year before implementation of federal incentives. Among physicians with e-prescribing capabilities, about a quarter used the technology only occasionally or not at all. Moreover, fewer than 60 percent of physicians with e-prescribing had access to three advanced features included as part of the Medicare and Medicaid incentive programs--identifying potential drug interactions, obtaining formulary information and transmitting prescriptions to pharmacies electronically--and less than a quarter routinely used all three features. Physicians in practices using electronic medical records exclusively were much more likely to report routine use of e-prescribing than physicians with stand-alone e-prescribing. systems. Other gaps in adoption and routine use of e-prescribing also exist, most notably between physicians in larger and smaller practices
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".