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

  • Margaret LowensteinEmail author
  • Erik Hossain
  • Wei Yang
  • David Grande
  • Jeanmarie Perrone
  • Mark D. Neuman
  • Michael Ashburn
  • M. Kit Delgado
Original Research



Prescribing limits are one policy strategy to reduce short-term opioid prescribing, but there is limited evidence of their impact.


Evaluate implementation of a state prescribing limit law and health system electronic medical record (EMR) alert on characteristics of new opioid prescriptions, refill rates, and clinical encounters.


Difference-in-differences study comparing new opioid prescriptions from ambulatory practices in New Jersey (NJ) to controls in Pennsylvania (PA) from 1 year prior to the implementation of a NJ state prescribing limit (May 2016–May 2017) to 10 months after (May 2017–March 2018).


Adults with new opioid prescriptions in an academic health system with practices in PA and NJ.


State 5-day opioid prescribing limit plus health system and health system EMR alert.

Main Measures

Changes in morphine milligram equivalents (MME) and tablet quantity per prescription, refills, and encounters, adjusted for patient and prescriber characteristics.

Key Results

There were a total of 678 new prescriptions in NJ and 4638 in PA. Prior to the intervention, median MME/prescription was 225 mg in NJ and 150 mg in PA, and median quantity was 30 tablets in both. After implementation, median MME/prescription was 150 mg in both states, and median quantity was 20 in NJ and 30 in PA. In the adjusted model, there was a greater decrease in mean MME and tablet quantity in NJ relative to PA after implementation of the policy plus alert (− 82.99 MME/prescription, 95% CI − 148.15 to − 17.84 and − 10.41 tabs/prescription, 95% CI − 19.70 to − 1.13). There were no significant differences in rates of refills or encounters at 30 days based on exposure to the interventions.


Implementation of a prescribing limit and EMR alert was associated with an approximately 22% greater decrease in opioid dose per new prescription in NJ compared with controls in PA. The combination of prescribing limits and alerts may be an effective strategy to influence prescriber behavior.


health policy health services research opioid addiction 



Contributors: The authors would like to thank the executive directors of the University of Pennsylvania Health System Opioid Task Force, David Horowitz and John Sestito, and the leadership of the Information Technology Subcommittee, Christine Vanzandbergen and John T. Howell, for their operational and logistical contributions to this project.

Funding Information

Dr. Lowenstein is funded by the Department of Veterans Affairs through the National Clinician Scholars Program. Additional funding was provided by the Division of General Internal Medicine at the University of Pennsylvania School of Medicine pilot grant (Dr. Lowenstein), NIH K23HD090272001 (Dr. Delgado), and NIH R01DA042299-02 (Dr. Neuman).

Compliance with Ethical Standards

The study was approved by the University of Pennsylvania Institutional Review Board.

Conflict of Interest

Dr. Delgado reports receiving an honorarium for participating in an Expert Roundtable on Innovations in Acute Pain Management convened by United Health Group in 2017. He has no current financial conflicts of interest. Dr. Ashburn reported receiving personal fees from the Department of Justice, the Attorney General for the State of Maryland, the Department of State for the Commonwealth of Pennsylvania, the Montgomery County District Attorney, and the Carolinas Pain Society. All remaining authors declare that they do not have a conflict of interest.

Supplementary material

11606_2019_5302_MOESM1_ESM.docx (44 kb)
ESM 1 (DOCX 43 kb)


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Copyright information

© Society of General Internal Medicine (This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply) 2019

Authors and Affiliations

  • Margaret Lowenstein
    • 1
    • 2
    • 3
    Email author
  • Erik Hossain
    • 4
  • Wei Yang
    • 5
  • David Grande
    • 1
    • 3
    • 6
  • Jeanmarie Perrone
    • 3
    • 7
  • Mark D. Neuman
    • 3
    • 8
  • Michael Ashburn
    • 3
    • 8
  • M. Kit Delgado
    • 3
    • 5
    • 7
  1. 1.National Clinician Scholars Program Perelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of MedicineCorporal Michael J. Crescenz Veterans Affairs Medical CenterPhiladelphiaUSA
  3. 3.The Leonard Davis Institute of Health Economics University of PennsylvaniaPhiladelphiaUSA
  4. 4.Data Analytics Center, Penn MedicinePhiladelphiaUSA
  5. 5.Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  6. 6.Division of General Internal MedicinePerelman School of School of Medicine at the University of PennsylvaniaPhiladelphiaUSA
  7. 7.Center for Emergency Care Policy and Research, Department of Emergency MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA
  8. 8.Department of Anesthesia and Critical CarePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA

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