Prescription Default Nudges for Opioid Reduction after Major Surgery (NORMS): A Randomized Controlled Trial

We aimed to evaluate the impact of an electronic health record (EHR)-based default "nudge" intervention on opioid prescribing after common surgical operations.

Given ongoing national challenges in opioid use and opioid-related adverse events, there is a need to optimize opioid prescribing after surgery. Behavioral nudges built into the EHR may be effective and useful, but they have not been widely tested among surgeons.

This is a randomized clinical trial at a large academic medical center involving adult surgical patients. System-wide, 118 surgical clinicians were randomized to control or intervention versions of an electronic discharge order set; the intervention arm had suggested pre-populated opioid and adjunct prescriptions. The outcome was morphine milligram equivalents (MME) prescribed at discharge, analyzed at the surgical encounter level.

663 surgical encounters (377 intervention and 286 control) were analyzed. 57% of the patients were female, and the median age was 49 years. The most common operations were laparoscopic cholecystectomy (39%), laparoscopic appendectomy (25%), and laparoscopic/robotic colectomy (12%). The median opioids prescribed at discharge was 75 MME (~ 10 oxycodone-5 mg tablets). Overall, in the intention-to-treat analysis, there were similar MME prescribed in the control and intervention groups (adjusted difference: 2.4 MME, 95% CI: -14.7 to 19.4, P=0.79). Only 21% in the intervention group received prescriptions from pre-populated defaults, while 79% received free form prescriptions. In as-treated analysis, the pre-populated prescriptions were substantially smaller than those written free form (adjusted difference: -22.5 MME, 95% CI: -34.7 to -10.2).

The offering of pre-populated opioid prescriptions to surgeons did not reduce postoperative opioid prescribing overall. However, the subset of surgeons who accepted the defaults prescribed much less opioids. Default nudge interventions may be useful in optimizing opioid prescribing, but stronger versions of defaults and co-interventions are likely needed.