Implementing Electronic Health Record Default Settings to Reduce Opioid Overprescribing: A Pilot Study

• Published in: Pain medicine (Malden, Mass.) Vol. 20; no. 1; pp. 103 - 112
• Main Authors: Zivin, Kara, White, Jessica O, Chao, Sandra, Christensen, Anna L, Horner, Luke, Petersen, Dana M, Hobbs, Morgan R, Capreol, Grace, Halbritter, Kevin A, Jones, Christopher M
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2019
• Subjects: Acute pain; Analysis; Electronic medical records; Electronic records; Health; Medical errors; Medical records; Narcotics; Opioids; Pain management; Patient safety; Prescription writing; Prescriptions; Prevention; Michigan; United States > US; Acute Pain; Analgesic; Narcotics; Prescriptions; Opioids
• ISSN: 1526-2375; 1526-4637
• DOI: 10.1093/pm/pnx304

Abstract Objective To pilot test the effectiveness, feasibility, and acceptability of instituting a 15-pill quantity default in the electronic health record for new Schedule II opioid prescriptions. Design A mixed-methods pilot study in two health systems, including pre-post analysis of prescribed opioid quantity and focus groups or interviews with prescribers and health system administrators. Methods We implemented a 15-pill electronic health record default for new Schedule II opioids and assessed opioid quantity before and after implementation using electronic health record data on 6,390 opioid prescriptions from 448 prescribers. We then analyzed themes from focus groups and interviews with four staff members and six prescribers. Results The proportion of opioid prescriptions for 15 pills increased at both sites after adding an electronic health record default, with one reaching statistical significance (from 4.1% to 7.2% at CHC, P = 0.280, and 15.9% to 37.2% at WVU, P < 0.001). The proportion of 15-pill prescriptions increased among high-prescribing departments and among most high- and low-frequency prescribers, except for low-frequency prescribers at CHC. Sites reported limited challenges in instituting the default, although ease of implementation varied by electronic health record vendor. Most prescribers were not aware of the default change and stated that they made prescribing decisions based on patient clinical characteristics rather than defaults. Conclusions This pilot provides initial evidence that changing default settings can increase the number of prescriptions at the default level. This low-cost and relatively simple intervention could have an impact on opioid overprescribing. However, default settings should be selected carefully to avoid unintended consequences.