Prescription Drug Monitoring Program Mandates: Impact On Opioid Prescribing And Related Hospital Use
Bibliographic record
Abstract
Comprehensive mandates for prescription drug monitoring programs (PDMPs) require state-licensed prescribers and dispensers both to register with and to use the programs in most clinical circumstances. Such mandates have the potential to improve providers' participation and reduce opioid-related adverse events. Using Medicaid prescription data and hospital utilization data across the US in the period 2011-16, we found that state implementation of comprehensive PDMP mandates was associated with a reduction in the opioid prescription rate from 161.47 to 147.07 per 1,000 enrollees per quarter, a reduction in the opioid-related inpatient stay rate from 97.50 to 93.34 per 100,000 enrollees per quarter, and a reduction in the opioid-related emergency department (ED) visit rate from 74.60 to 61.36 per 100,000 enrollees per quarter. Our estimated annual reductions of approximately 12,000 inpatient stays and 39,000 ED visits could save over $155 million in Medicaid spending, a fact that deserves policy attention when states attempt to strengthen and refine PDMPs to better tackle the opioid crisis.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".