Health Care Provider Utilization of Prescription Monitoring Programs: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To synthesize the literature on the proportion of health care providers who access and use prescription monitoring program data in their practice, as well as associated barriers to the use of such data. DESIGN: We performed a systematic review using a standard systematic review method with meta-analysis and qualitative meta-summary. We included full-published peer-reviewed reports of study data, as well as theses and dissertations. METHODS: We identified relevant quantitative and qualitative studies. We synthesized outcomes related to prescription monitoring program data use (i.e., ever used, frequency of use). We pooled the proportion of health care providers who had ever used prescription monitoring program data by using random effects models, and we used meta-summary methodology to identify prescription monitoring program use barriers. RESULTS: Fifty-three studies were included in our review, all from the United States. Of these, 46 reported on prescription monitoring program use and 32 reported on barriers. The pooled proportion of health care providers who had ever used prescription monitoring program data was 0.57 (95% confidence interval: 0.48-0.66). Common barriers to prescription monitoring program data use included time constraints and administrative burdens, low perceived value of prescription monitoring program data, and problems with prescription monitoring program system usability. CONCLUSIONS: Our study found that health care providers underutilize prescription monitoring program data and that many barriers exist to prescription monitoring program data use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".