The impact of automatic physician review of prescription drug monitoring program data on opioid prescribing in a Maryland statewide hospital system

Authors

DOI:

https://doi.org/10.5055/jom.2022.0750

Keywords:

prescription opioids, policy, physician drug monitoring program, interrupted time series, healthcare system

Abstract

Background and aims: Prescription drug monitoring programs (PDMPs) were implemented to decrease dangerous opioid prescribing but have had variable results. This report details how automatic PDMP review changed opioid prescribing across a statewide medical system.

Design: An observational study.

Setting: Fourteen hospital networks in the United States.

Cases: Healthcare encountered from July 1, 2016 to June 30, 2019.

Intervention: Starting from July 1, 2018, the patient’s PDMP data would be displayed automatically to providers in the unified electronic medical record (EMR) whenever the provider began to write for an opioid prescription.

Measurements: Outcomes were prescriptions per encounter (PPE) and the morphine milligram equivalents (MME) per prescription. Outcomes were stratified by practice location, medication prescribed, and diagnosis. All data, including whether the prompt was triggered for a given encounter and whether a prescription was given, were extracted from the EMR. An interrupted time-series analysis was used to determine how PPE and MME changed in response to the implementation of automatic PDMP review.

Findings: Of the 624,488 encounters examined, 18.37 percent (n = 114,710) were in emergency departments, 56.79 percent were admissions (n = 354,634), and 24.84 percent (n = 155,144) were outpatient visits. Opioid prescriptions were started and then canceled 24 percent of the time after the PDMP was shown. There was a decline in MME (βOverall + Policy Trends = –3.17, p = <0.0001), which was driven by inpatient (βOverall + Policy Trends = –2.10, p < 0.0001) and outpatient providers (βOverall + Policy Trends = –3.24, p < 0.01). A decline in MME was seen in all medication categories (–1.72 < βOverall + Policy Trends < –5.87, p < 0.01). There were no changes in these trends after excluding encounters for severe and acute pain.

Conclusions: Automated PDMP review is associated with fewer prescriptions and smaller doses without decreasing appropriate use.

Author Biographies

Benoit Stryckman, MA

Department of Emergency Medicine, University of Maryland School of Medicine, University of Maryland, Baltimore, Baltimore, Maryland

Anna Schoenbaum, DNP

Corporate Information Services, Penn Medicine-University of Pennsylvania Health System, Philadelphia, Pennsylvania

Joel Klein, MD

University of Maryland Medical System, Maryland

Rose Chasm, MD

Department of Emergency Medicine, University of Maryland School of Medicine, University of Maryland, Baltimore, Baltimore, Maryland

Eberechukwu Onukwugha, PhD

Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, University of Maryland, Baltimore, Baltimore, Maryland

Zachary DW Dezman, MD

Department of Emergency Medicine; Department of Epidemiology and Public Health, University of Maryland School of Medicine, University of Maryland, Baltimore, Baltimore, Maryland

References

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Published

11/01/2022

How to Cite

Stryckman, MA, B., A. Schoenbaum, DNP, J. Klein, MD, R. Chasm, MD, E. Onukwugha, PhD, and Z. D. Dezman, MD. “The Impact of Automatic Physician Review of Prescription Drug Monitoring Program Data on Opioid Prescribing in a Maryland Statewide Hospital System”. Journal of Opioid Management, vol. 18, no. 6, Nov. 2022, pp. 547-56, doi:10.5055/jom.2022.0750.