Improving Patient Safety through Computerized Drug Management: The Devil Is in the Details
Bibliographic record
Abstract
Electronic prescribing and computerized drug management can improve the safety, quality and cost-effectiveness of prescribing. However, if the problems that lead to avoidable adverse events are not addressed by information technology, there is a risk of making considerable investment without the expected return of error reduction and improved patient safety. Improving the safety of prescribing is particularly important in ambulatory care, where most drugs are prescribed. To improve patient safety, IT solutions should be developed that provide: (1) access to the list of all currently active drugs, (2) alerts for relevant prescribing problems (therapeutic duplication, excess dose, dose adjustment for weight (children, elderly) and renal impairment, drug-disease, drug-drug, drug-age and drug-allergy contraindications), (3) the capacity to electronically submit medication stop orders to the dispensing pharmacy and (4) integration of electronic prescriptions (e-rx) into pharmacy software to avoid transcription errors. To improve quality of prescribing, IT solutions should be capable of providing physicians with reminders and alerts for evidence-based preventive care and disease management based on patient-specific drug, disease, therapeutic intent and other relevant clinical information. To improve the cost-effectiveness of prescribing, IT solutions should be developed to provide the cost of medication at the time the prescription is written, and evidence-based alerts for drugs of choice recommendations when appropriate.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".