An analysis of errors, discrepancies, and variation in opioid prescriptions for adult outpatients at a teaching hospital

Mark C. Bicket, MD; Deepa Kattail, MD; Myron Yaster, MD; Christopher L. Wu, MD; Peter Pronovost, MD, PhD

doi:10.5055/jom.2017.0367

Authors

Mark C. Bicket, MD
Deepa Kattail, MD
Myron Yaster, MD
Christopher L. Wu, MD
Peter Pronovost, MD, PhD

DOI:

https://doi.org/10.5055/jom.2017.0367

Keywords:

opioid analgesics, pain medication, medication use, prescription error

Abstract

Objective: To determine opioid-prescribing patterns and rate of three types of errors, discrepancies, and variation from ideal practice.

Design: Retrospective review of opioid prescriptions processed at an outpatient pharmacy.

Setting: Tertiary institutional medical center.

Patients: We examined 510 consecutive opioid medication prescriptions for adult patients processed at an institutional outpatient pharmacy in June 2016 for patient, provider, and prescription characteristics.

Main Outcome Measure(s): We analyzed prescriptions for deviation from best practice guidelines, lack of two patient identifiers, and noncompliance with Drug Enforcement Agency (DEA) rules.

Results: Mean patient age (standard deviation) was 47.5 years (17.4). The most commonly prescribed opioid was oxycodone (71 percent), usually not combined with acetaminophen. Practitioners prescribed tablet formulation to 92 percent of the sample, averaging 57 (47) pills. We identified at least one error on 42 percent of prescriptions. Among all prescriptions, 9 percent deviated from best practice guidelines, 21 percent failed to include two patient identifiers and 41 percent were noncompliant with DEA rules. Errors occurred in 89 percent of handwritten prescriptions, 0 percent of electronic health record (EHR) computer-generated prescriptions, and 12 percent of non-EHR computer-generated prescriptions. Interrater reliability by κ was 0.993.

Conclusions: Inconsistencies in opioid prescribing remain common. Handwritten prescriptions continue to demonstrate higher associations of errors, discrepancies, and variation from ideal practice and government regulations. All computer-generated prescriptions adhered to best practice guidelines and contained two patient identifiers, and all EHR prescriptions were fully compliant with DEA rules.

Author Biographies

Mark C. Bicket, MD

Assistant Professor, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland

Deepa Kattail, MD

Assistant Professor, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland

Myron Yaster, MD

Professor, Children’s Hospital Colorado, University of Colorado-Anschutz Medical Campus, Aurora, Colorado

Christopher L. Wu, MD

Professor, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland

Peter Pronovost, MD, PhD

Professor, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;Department of Surgery, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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