Inappropriate Prescribing Practices: The Challenge and Opportunity for Patient Safety
Bibliographic record
Abstract
Adverse clinical events related to inappropriate prescribing practices are an important threat to patient safety. Avoidance of inappropriate prescribing in community settings, where the majority of prescriptions are written, offers a major area of opportunity to improve quality of care and outcomes. Electronic medication order entry systems, with automated clinical risk screening and online alerting capabilities, appear as particularly promising enabling tools in such settings. The Medical Office of the Twenty First Century (MOXXI-III) research group is currently utilizing such a system that integrates identification of dosing errors, adverse drug interactions, drug-disease and allergy contraindications and potential toxicity or contraindications based on patient age. This paper characterizes the spectrum of alerts in an urban community of care involving 28 physicians and 32 pharmacies. Over a consecutive nine-month period, alerts were generated in 29% of 22,419 prescriptions, resulting in revised prescriptions in 14% of the alert cases. Drug-disease contraindications were the most common driver of alerts, accounting for 41% of the total and resulting in revised prescriptions in 14% of cases. In contrast, potential dosing errors generated only 8% of all alerts, but resulted in revised prescriptions 23% of the time. Overall, online evidence-based screening and alerting around prescription of medications in a community setting demands confirmation in prescribers' clinical decision making in almost one-third of prescriptions and leads to changed decisions in up to one-quarter of some prescribing categories. Its ultimate determination of clinical relevance to patient safety may, however, have to await more detailed examination of physician response to alerts and patient outcomes as a primary measure of utility. Patient safety is an increasingly recognized challenge and opportunity for stakeholders in improving health care delivery. It involves many issues, including delayed diagnosis and treatment, as well as inappropriate undertreatment and overtreatment. The common denominators, however, are that care and outcomes could be better, and there is a role for patients, providers and policy makers in making improvements.
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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 | 0.039 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".