Abstract
BACKGROUND: Patients undergoing oncologic surgery are at risk for persistent postoperative opioid use. As a quality improvement initiative, this study sought to characterize provider perceptions regarding opioid-prescribing after oncologic procedures. METHODS: Surgical oncology attending physicians, clinical fellows, and advanced practice providers (APPs) at a high-volume cancer center were surveyed before and after educational sessions focusing on the opioid epidemic with review of departmental data. RESULTS: The pre-education response rates were 72 (70%) of 103: 22 (65%) of 34 attending physicians, 19 (90%) of 21 fellows, and 31 (65%) of 48 APPs. For five index operations (open abdominal resection, laparoscopic colectomy, wide local excision, thyroidectomy, port), the fellows answered that patients should stop receiving opioids sooner than recommended by the attending surgeons or APPs. For four of five procedures, the APPs recommended higher discharge opioid prescriptions than the attending surgeons or fellows. Almost half of the providers (n = 46, 45%) responded to both the pre- and post-education surveys. After the intervention, the providers recommended lower numbers of opioid pills and indicated that patients should be weaned from opioids sooner for all the procedures. Compared with pre-education, more providers agreed post-education that discharge opioid prescriptions should be based on a patient's last 24 h of inpatient opioid use (83 vs 91%; p = 0.006). The providers who did not attend a session showed no difference in perceptions or recommendations at the repeat assessment. CONCLUSIONS: Variation exists in perioperative opioid-prescribing among provider types, with those most involved in daily care and discharge processes generally recommending more opioids. After education, providers lowered discharge opioid recommendations and thought patients should stop receiving opioids sooner. The next steps include assessing for quantitative changes in opioid-prescribing and implementing standardized opioid prescription algorithms.