55 If you don't use it, lose it: reducing over prescription of opioids at discharge

Abstract

Objectives: To decrease the median number of opioid tabs prescribed to gynecologic oncology patients after open surgery who use zero oral morphine equivalents (“0-OME”) the day before hospital discharge. Methods: At our academic institution, medical trainees are invited annually to design and implement interprofessional quality improvement (QI) projects. An evidence-based post-discharge opioid prescription size calculator was developed within our division. The data used to make the calculator found that 35% of patients after laparotomy in 2021 used 0-OME the day prior to discharge, 97% of those patients were given a prescription for a median of 10 doses of 5mg oxycodone tabs and their median at home use of opioids was zero doses, leading to an excess of opioids in the community. We identified this pattern of over-prescription as an opportunity for a QI subcomponent to the calculator project. We utilized the post-discharge opioid calculator, which recommends a discharge prescription of 1–2 tabs of 5mg oxycodone (or equivalent alternative opioid) for 0-OME patients. Our project asked our inpatient team to use the opioid calculator to determine an appropriate post-discharge prescription size for 0-OME patients. Our initial in-process metric was completion of a fellow-led educational session with each team of rotating residents in which we reviewed our project aim and the implications of opioid over-prescription. Results: Between August 2022-March 2023 there were 37 0-OME patients discharged after open surgery. These patients were prescribed a median of 2 doses of 5mg oxycodone tabs (or equivalent alternative opioid) at discharge, which was an 80% decrease compared to 2021 historical controls. The education sessions were completed for 100% of the eight rotating resident teams. In our first plan-do-study-act (PDSA) cycle we realized we did not know if providers were utilizing the opioid calculator. Thus, we added an additional in process metric to track intervention fidelity by including documentation of calculator use in the discharge summary. After this change, 85% of discharge summaries for 0-OME patients documented calculator use. Conclusions: We met our goal of reducing the median opioid prescription size for 0-OME patients. We also demonstrated the feasibility of fellow led education for implementation and through the PDSA approach, identified the need for a new in process metric tracking calculator use.