Background: Adherence to opioid prescribing protocols after operations remains challenging despite published guidelines. Integration of these guidelines with the electronic health record could potentially improve their adoption. We hypothesize that implementing an electronic health record order set containing prepopulated tablet quantities tailored to surgical procedures based on published guidelines will decrease postoperative opioid prescription.
Methods: We conducted a 12-month prepost intervention study on adult patients who underwent appendectomy, cholecystectomy, inguinal or umbilical hernia repair, thyroidectomy, or parathyroidectomy at a single institution. An electronic health record order set was developed with prepopulated opioid tablet quantities reflecting the upper limit of published recommendations. The primary endpoint was change in morphine milligram equivalent prescribed postintervention and was analyzed using linear regression adjusting for age, race, procedure, and prescriber training level. Secondary endpoints were emergency department visits for pain-related issues and opioid refill rates.
Results: We identified 524 patients (mean age = 53, 51% male) in our baseline cohort and 433 patients (mean age = 52, 58% male) in our postintervention group. The mean morphine milligram equivalent prescribed was 62.6 and 50.4 for the preintervention and postintervention cohorts, respectively (P = .049). Thyroidectomies and parathyroidectomies achieved the largest decrease after intervention, which decreased to 42.6 morphine milligram equivalent from 79.7 morphine milligram equivalent preintervention (P < .001). Refill rate was 1.6% postintervention compared to 3.1% preintervention (P = .20), and emergency department visit for pain control rate was 0.2% post intervention and 2.5% preintervention (P = .005).
Conclusion: An electronic health record tailored order set based on prescription guidelines is a safe, effective, and scalable intervention for decreasing opioid prescriptions after operations.
Copyright © 2022 Elsevier Inc. All rights reserved.