Objectives/Hypothesis: To produce a sustained reduction in opioid prescriptions in patients <5 years of age undergoing T&A through utilization of standardized algorithms and electronic health record (EHR) automation tools. Study Design: Prospective quality improvement initiative. Methods: Plan-do-study-act (PDSA) methodology was used to design an age-based postoperative pain regimen in which children <5 years of age received a non-opioid pain regimen, and option to prescribe oxycodone for additional pain relief was given for children >5 years of age. Standardized discharge instructions and automated, age-specific order sets were created to facilitate adherence. Rate of discharge opioid prescription was monitored and balanced against post-discharge opioid prescriptions and returns to the emergency department (ED). Results: In children <5 years of age undergoing T&A, reduction in opioid prescription rates from 65.9% to 30.9% after initial implementation of the order set was noted. Ultimately, reduction of opioid prescribing rates to 3.7% of patients was noted after pain-regimen consensus and EHR order set implementation. Opioid prescriptions in patients >5 years of age decreased from 90.6% to 58.1% initially, and then down 35.9% by the last time point analyzed. Requests for outpatient opioid prescriptions did not increase. There was no significant change in returns to the emergency ED for pain management, or in the number opioids prescribed when patients returned to the ED. Conclusions: Iterative cycles of improvement utilizing standardized pain management algorithms and EHR tools were effective means of producing a sustained reduction in opioid prescriptions in postoperative T&A patients. Such findings suggest a framework for similar interventions in other pediatric otolaryngology settings. Level of Evidence: 4 Laryngoscope, 131:E2337–E2343, 2021.
- Quality improvement projects
- opioid prescribing
- pediatric adenotonsillectomy
- plan-do-study-act methodology
- postoperative pain management
ASJC Scopus subject areas