Pharmaceutical Automated Reporting: An opioid stewardship tool
Bibliographic record
Abstract
OBJECTIVE: To develop and implement a customized clinical decision support system (CDSS) in an under-resourced health region aimed at promoting appropriate and safe opioid prescribing. DESIGN: The Pharmaceutical Automated Reporting (PAR) tool integrates inpatient prescription data from BDM Pharmacy (version 10) and categorizes patient information using predefined logic. It operates with Python (version 3.10) and Microsoft Excel®, functioning as decision trees. Nine risk factors (absence of naloxone prescription with an opioid prescription, naloxone administration, high-frequency opioid dosing, multiple opioids prescribed, concurrent benzodiazepine and opioid coprescribed, over 7 days of intravenous route opioid use, morphine equivalent dose received over or equal to 90, possible opioid agonist therapy, possible alcohol withdrawal therapy) are assessed through a decision matrix to classify patients for opioid-related risk. RESULTS: Over 7 months, the PAR tool detected one opioid-related risk factor in 98.9 percent (n = 10,450) of patients prescribed an opioid and multiple risk factors in 62.4 percent (n = 6,590). The tool identified areas where data-driven interventions by the Opioid Stewardship Program could promote appropriate prescribing practices and will be used to track and promote stewardship interventions, inform policy change, and evaluate the impact on quality indicators. CONCLUSION: Small, resource-scarce health systems can use open-source programming methodologies to create an internal CDSS to assist in addressing opioid-related risk factors within their healthcare facilities.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".