BACKGROUND: The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption. OBJECTIVE: To address excess opioid prescribing within our institutions, we applied a plan-do-study-act (PDSA)-like quality improvement strategy to assess local opioid prescribing and use, modify our institutional protocols, and assess the impacts of the change. The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption. We describe our approach, findings, and lessons learned from our quality improvement approach. METHODS: We prospectively recorded home pain pill usage after robotic-assisted laparoscopic prostatectomy (RALP) and robotic-assisted partial nephrectomy (RAPN) at two academic institutions from July 2016 to July 2019. Patients prospectively recorded their home pain pill use on a take-home log. Other factors, including numeric pain rating scale on the day of discharge, were extracted from patient records. We analyzed our data and modified opioid prescription protocols to meet the reported use data of 80% of patients. We continued collecting data after the protocol change. We also used our prospectively collected data to assess the accuracy of a retrospective phone survey designed to measure postdischarge opioid use. Our primary outcomes were the proportion of patients taking zero opioid pills postdischarge, median pills taken after discharge and the number of excess pills prescribed but not taken. We compared these outcomes before and after protocol change. RESULTS: A total of 266 patients (193 RALP, 73 RAPN) were included. Reducing the standard number of prescribed pills did not increase the percentage of patients taking zero pills postdischarge in either group (RALP: 47% vs. 41%; RAPN 48% vs. 34%). The patients in either group reporting postoperative Day 1 pain score of 0 or 1 were much more likely to use zero postdischarge opioid pills. Our reduction in prescribing protocol resulted in an estimated reduction in excess pills from 1555 excess pills in the prior protocol to just 155 excess pills in the new protocol. CONCLUSION: Our PDSA-like approach led to an acceptable protocol revision resulting in significant reductions in excess pills released into the community. Reducing the quantity of opioids prescribed postoperatively does not increase the percentage of patients taking zero pills postdischarge. To eliminate opioid use may require no-opioid pathways. Our approach can be used in implementing zero opioid discharge plans and can be applied to opioid reduction interventions at other institutions where barriers to reduced prescribing exist.
BACKGROUND: The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption. OBJECTIVE: To address excess opioid prescribing within our institutions, we applied a plan-do-study-act (PDSA)-like quality improvement strategy to assess local opioid prescribing and use, modify our institutional protocols, and assess the impacts of the change. The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption. We describe our approach, findings, and lessons learned from our quality improvement approach. METHODS: We prospectively recorded home pain pill usage after robotic-assisted laparoscopic prostatectomy (RALP) and robotic-assisted partial nephrectomy (RAPN) at two academic institutions from July 2016 to July 2019. Patients prospectively recorded their home pain pill use on a take-home log. Other factors, including numeric pain rating scale on the day of discharge, were extracted from patient records. We analyzed our data and modified opioid prescription protocols to meet the reported use data of 80% of patients. We continued collecting data after the protocol change. We also used our prospectively collected data to assess the accuracy of a retrospective phone survey designed to measure postdischarge opioid use. Our primary outcomes were the proportion of patients taking zero opioid pills postdischarge, median pills taken after discharge and the number of excess pills prescribed but not taken. We compared these outcomes before and after protocol change. RESULTS: A total of 266 patients (193 RALP, 73 RAPN) were included. Reducing the standard number of prescribed pills did not increase the percentage of patients taking zero pills postdischarge in either group (RALP: 47% vs. 41%; RAPN 48% vs. 34%). The patients in either group reporting postoperative Day 1 pain score of 0 or 1 were much more likely to use zero postdischarge opioid pills. Our reduction in prescribing protocol resulted in an estimated reduction in excess pills from 1555 excess pills in the prior protocol to just 155 excess pills in the new protocol. CONCLUSION: Our PDSA-like approach led to an acceptable protocol revision resulting in significant reductions in excess pills released into the community. Reducing the quantity of opioids prescribed postoperatively does not increase the percentage of patients taking zero pills postdischarge. To eliminate opioid use may require no-opioid pathways. Our approach can be used in implementing zero opioid discharge plans and can be applied to opioid reduction interventions at other institutions where barriers to reduced prescribing exist.
Authors: Kevin Koo; Farzana Faisal; Natasha Gupta; Alexa R Meyer; Hiten D Patel; Phillip M Pierorazio; Brian R Matlaga Journal: J Urol Date: 2019-08-29 Impact factor: 7.450
Authors: Heidi N Overton; Marie N Hanna; William E Bruhn; Susan Hutfless; Mark C Bicket; Martin A Makary Journal: J Am Coll Surg Date: 2018-08-14 Impact factor: 6.113
Authors: Hiten D Patel; Arnav Srivastava; Neil D Patel; Farzana A Faisal; Wesley Ludwig; Gregory A Joice; Zeyad R Schwen; Mohamad E Allaf; Misop Han; Amin S Herati Journal: Eur Urol Date: 2018-10-21 Impact factor: 20.096
Authors: Bruce L Jacobs; Devin Rogers; Jonathan G Yabes; Jathin Bandari; Omar M Ayyash; Avinash Maganty; Kody M Armann; Hermoon A Worku; Natalie M Pace; Anup Shah; Kelly R Pekala; Michelle Yu; Jacques E Chelly; Liam C Macleod; Benjamin J Davies Journal: Cancer Date: 2020-10-01 Impact factor: 6.860