Impact of a State Opioid Prescribing Limit and Electronic Medical Record Alert on Opioid Prescriptions: a Difference-in-Differences Analysis

Lowenstein, Margaret; Hossain, Erik; Yang, Wei; Grande, David; Perrone, Jeanmarie; Neuman, Mark D.; Ashburn, Michael A.; Delgado, M. Kit

doi:10.1007/s11606-019-05302-1

Cited by 35 publications

(22 citation statements)

References 29 publications

(30 reference statements)

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“…Another approach to reducing overprescribing, especially among the outliers, is incorporating a system into the electronic health records that limits the number of opioid pills and total MME for patients being discharged from the hospital or ER. 34…”

Section: Discussionmentioning

confidence: 99%

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns

et al. 2020

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BACKGROUND: Prescription opioid overprescribing is a focal point for legislators, but little is known about opioid prescribing patterns of primary care nurse practitioners (NPs) and physician assistants (PAs). OBJECTIVE: To identify prescription opioid overprescribers by comparing prescribing patterns of primary care physicians (MDs), nurse practitioners (NPs), and physician assistants (PAs). DESIGN: Retrospective, cross-sectional analysis of Medicare Part D enrollee prescription data. PARTICIPANTS: Twenty percent national sample of 2015 Medicare Part D enrollees. MAIN MEASURES: We identified potential opioid overprescribing as providers who met at least one of the following:(1) prescribed any opioid to > 50% of patients, (2) prescribed ≥ 100 morphine milligram equivalents (MME)/ day to > 10% of patients, or (3) prescribed an opioid > 90 days to > 20% of patients. KEY RESULTS: Among 222,689 primary care providers, 3.8% of MDs, 8.0% of NPs, and 9.8% of PAs met at least one definition of overprescribing. 1.3% of MDs, 6.3% of NPs, and 8.8% of PAs prescribed an opioid to at least 50% of patients. NPs/PAs practicing in states with independent prescription authority were > 20 times more likely to overprescribe opioids than NPs/PAs in prescriptionrestricted states. CONCLUSIONS: Most NPs/PAs prescribed opioids in a pattern similar to MDs, but NPs/PAs had more outliers who prescribed high-frequency, high-dose opioids than did MDs. Efforts to reduce opioid overprescribing should include targeted provider education, risk stratification, and state legislation.

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Section: Discussionmentioning

confidence: 99%

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns

et al. 2020

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“…15 Our results showing decreased adverse effects rate following the policy change are consistent with other studies reporting decreased prescribing following similar policy changes in the Eastern USA. 11,12,25,26 A novel finding Notes: IRR, incidence rate ratio; CI, confidence interval; * P<.0005; time pre-policy is the slope in the pre-opioid prescription policy time period; time post-vs. pre-policy is the difference in slope in the post-vs. the pre-opioid prescription policy time periods; policy indicator is the difference in the levels of the outcome in the post-vs. the pre-time periods Notes: IRR, incidence rate ratio; CI, confidence interval; * P<.01, † P=.049; time pre-policy is the slope in the pre-opioid prescription policy time period; time post-vs. pre-policy is the difference in slope in the post-vs. the pre-opioid prescription policy time periods; policy indicator is the difference in the levels of the outcome in the post-vs. the pre-time periods; Chronic: patients with a history of a chronic opioid prescription in the leadin period; Intermittent: patients with a history of intermittent opioid prescriptions in the lead-in period; None: patients with no history of an opioid prescription in the lead-in period within the electronic medical record system of the medical center from the current study is that opioid-naïve primary care patients and those with a history of chronic opioid prescriptions experienced the most dramatic reduction in the rate of opioidrelated adverse effects after the policy. We view these findings in relation to other efforts ongoing in Vermont and other states to improve treatment for opioid use disorder.…”

Section: Discussionmentioning

confidence: 98%

Influence of Opioid Prescription Policy on Overdoses and Related Adverse Effects in a Primary Care Population

et al. 2021

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“…Research on changing opioid prescriptions has emphasized number of pills prescribed, 26 or the number of long-versus short-term prescriptions, 27 with weaker focus on outcomes for patients, or on how health care organizations reduce or eliminate opioid prescribing for long-term recipients. Deimplementation has been defined as reducing or stopping services or practices that are ineffective, unproven, harmful, overused, or inappropriate.…”

Section: Considerations To Guide Future Implementation Science On Prescription Opioid Reductionmentioning

confidence: 99%

Promoting Patient-Centeredness in Opioid Deprescribing: a Blueprint for De-implementation Science

Kertesz

McCullough²,

Darnall

et al. 2020

J GEN INTERN MED

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A downward trend in opioid prescribing between 2011 and 2018 has brought per-capita opioid prescriptions below the levels of 2006, the earliest year for which the Centers for Disease Control and Prevention has published data. That trend has affected roughly ten million patients who previously received long-term opioid therapy. Any effort to reduce or replace a prior health practice is termed de-implementation. We suggest that the evaluation of opioid prescribing de-implementation has been misdirected, within US policy and health research, resulting in detrimental impacts on patients, their families and clinicians. Policymakers and implementation scientists can address these deficiencies in how we study and how we perform opioid de-implementation by applying an implementation science framework: the Consolidated Framework for Implementation Research. The Consolidated Framework lays out relevant domains of activity (internal, external, etc.) that influence implementation processes and outcomes. It can deepen our understanding of how policies a r e c h o s e n , c o m m u n i c a t e d , a n d c a r r i e d o u t . Policymakers and researchers who embrace this framework will need a better approach to measuring success and failure in health care where both pain and opioids are concerned. This would involve shifting from a reductive focus on opioid prescription counts toward measures that are more effective, holistic, and patient-centered.

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Impact of a State Opioid Prescribing Limit and Electronic Medical Record Alert on Opioid Prescriptions: a Difference-in-Differences Analysis

Cited by 35 publications

References 29 publications

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns

Influence of Opioid Prescription Policy on Overdoses and Related Adverse Effects in a Primary Care Population

Promoting Patient-Centeredness in Opioid Deprescribing: a Blueprint for De-implementation Science

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