Electronic prescribing in an ambulatory care setting: a cluster randomized trial
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Medication-prescribing errors with adverse drug events impose substantial harms on patients and health systems. Medication errors resulting in preventable adverse drug events most commonly occur at the ordering stage. Electronic prescribing may prevent such errors but its impact has not been rigorously evaluated. METHODS: We conducted a pragmatic cluster randomized controlled trial in academic hospital ambulatory clinics to evaluate the effects of a commercially available electronic prescribing software system on total prescription error ratio. Secondary outcomes included the number of callbacks for clarification from community pharmacies to physicians' clinics. RESULTS: Twenty-six physicians used the electronic prescribing system, writing 1980 prescriptions during 44 intervention weeks when the electronic prescribing system was available (7.6% of these were electronic, the remainder handwritten) and 973 prescriptions during 22 control weeks while the system was switched off (1.4% electronic, prescribed in the previous intervention week, but issued with delay). The total prescription error rate was 118/1980 (6.0%) in intervention weeks and 57/973 (5.9%) in control weeks (P = 0.91). During the intervention period more callbacks requesting clarification were made to clinic administrators (n = 83, 1.89 per week) than during control weeks (n = 32, 1.45 per week; P < 0.001). CONCLUSION: Implementation of the electronic prescribing system had no impact on total prescription error, and increased the callback rate. In spite of intensive user support, few prescriptions in intervention weeks were made using the electronic system. Given the costs, training requirements, workflow redesigns and regulatory hurdles, additional evaluations of outpatient prescribing on clinically important outcomes are needed.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".