The Development and Evaluation of an Integrated Electronic Prescribing and Drug Management System for Primary Care
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate the acceptability and use of an integrated electronic prescribing and drug management system (MOXXI) for primary care physicians. DESIGN: A 20-month follow-up study of MOXXI (Medical Office of the XXIst Century) implementation in 28 primary care physicians and 13,515 consenting patients. MEASUREMENT: MOXXI was developed to enhance patient safety by integrating patient demographics, retrieving active drugs from pharmacy systems, generating an automated problem list, and providing electronic prescription, stop order, automated prescribing problem alerts, and compliance monitoring functions. Evaluation of technical performance, acceptability, and use was conducted using audit trails, questionnaires, standardized tasks, and information from comprehensive health insurance databases. RESULTS: Perceived improvements in continuity of care and professional autonomy were associated with physicians' expected use of MOXXI. Physician speed in using MOXXI improved substantially in the first three months; however, only the represcribing function was faster using MOXXI than by handwritten prescription. Physicians wrote electronic prescriptions in 36.9 per 100 visits and reviewed the patient's drug profile in 12.6 per 100 visits. Physicians rated printed prescriptions, the current drug list, and the represcribing function as the most beneficial aspects of the system. Physicians were more likely to use the drug profile for patients who used more medication, made more emergency department visits, had more prescribing physicians, and lower continuity of care. CONCLUSION: Primary care physicians believed an integrated electronic prescribing and drug management system would improve continuity of care, and they were more likely to use the system for patients with more complex, fragmented care.
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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 | 0.007 | 0.023 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".