Evidence map›Paper›PMID 33484144›Full record

SynthesisPain medicine (Malden, Mass.)2021

Health Care Provider Utilization of Prescription Monitoring Programs: A Systematic Review and Meta-Analysis.

Alysia Robinson, Maria N Wilson, Jill A Hayden, Emily Rhodes, Samuel Campbell, Peter MacDougall, Mark Asbridge

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Pain medicine (Malden, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alysia RobinsonDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Maria N WilsonDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Jill A HaydenDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Emily RhodesDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Samuel CampbellDepartment of Emergency Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Peter MacDougallDepartment of Emergency Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Mark AsbridgeDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.

Funding

CIHR #397982
6 · The paper itself

Abstract

objectiveTo 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.

designWe 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.

methodsWe 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.

resultsFifty-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.

conclusionsOur study found that health care providers underutilize prescription monitoring program data and that many barriers exist to prescription monitoring program data use.

Indexed as

Prescription Drug Monitoring ProgramsAttitude of Health PersonnelHealth PersonnelHumansPractice Patterns, Physicians'Qualitative ResearchUnited StatesOpioidsPrescription Drug Monitoring ProgramsPrescription Monitoring ProgramsSystematic ReviewUtilization

Identifiers

PMID33484144
PMCPMC8311582

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.