ArticleDrug and alcohol dependence2019
Advanced visualizations to interpret prescription drug monitoring program information.
Article in Drug and alcohol dependence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in prescription drug monitoring program research: a literature synthesis (June 2018 to December 2019).Current opinion in psychiatry · 2020Pooled it
- Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.Research in social & administrative pharmacy : RSAP · 2023Article
- Optimization of Prescription Drug Monitoring Program to Overcome Opioid Epidemic in West Virginia.Cureus · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesPrescription drug monitoring programs (PDMPs) are state-based databases that contain information about controlled substance prescriptions dispensed by pharmacies. Many states now mandate PDMP use by prescribers, despite unclear effectiveness. We hypothesize that it is possible to improve the interpretability, and hence effectiveness, of PDMPs by enhancing them.
methodsThis was a real-time simulation of an enhanced PDMP. Fifty practicing physicians (25 primary care, 25 emergency medicine) were randomized to see three cases with a standard profile or an enhanced profile that included graphical representation of prescriptions and identified risky prescribing patterns. After a two-month washout period, participants were placed in the opposite group.
resultsPhysicians presented with the enhanced profile were more likely to correctly identify patients with multiple providers (97.0% vs. 85.8%, p = 0.002), overlapping opioid and benzodiazepine prescriptions (94.7% vs. 87.5%, p = 0.03), overlapping opioid prescriptions (89.5% vs. 70.8%, p < 0.01), high daily dosages of opioids (99.2% vs. 25.0%, p = 0.02), and traveling to distant pharmacies (79.7% vs. 2.5%, p < 0.01). There was no difference in interpretation time for the three cases (standard profile 657.3 s vs. enhanced profile 686.3 s, p = 0.31).
conclusionsA simulated PDMP with graphical displays and interpretation of findings was, for this cohort of emergency physicians and primary care physicians, associated with an increased ability to determine high-risk features on PDMP profiles.
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