PURPOSE: Long wait times are a common occurrence for chemotherapy infusion patients and are a source of decreased patient satisfaction. Our facility sought to decrease outpatient infusion clinic wait times by 20% using the Model for Improvement, quality improvement tools, and Plan-Do-Study-Act cycles. METHODS: A multidisciplinary team was formed to address clinic wait times. Patient interviews, time studies, process mapping, brainstorming sessions, affinity diagrams, fishbone diagrams, and surveys were used to define the problem and to develop an intervention. Wait times from check-in until medication administration were analyzed using statistical process control charts. Our Plan-Do-Study-Act cycle led to the addition of a “fast-track” clinic title for patients not waiting for laboratory results on the day of treatment and changes in clinic communication. The fast-track clinic signaled for those patients to have priority for vital sign collection and earlier notification to pharmacy to begin preparing medications. RESULTS: Baseline wait times for patients not requiring laboratories on the day of treatment averaged 1 hour and 33 minutes. After intervention, using statistical process control charts, a shift was observed with a new average wait time of 1 hour and 12 minutes (a 23% decrease). Wait times for patients requiring laboratories on the day of treatment did not change significantly. CONCLUSION: Implementation of a fast-track clinic title and improving communication resulted in a significant reduction in wait times for patients not requiring laboratories on the day of treatment. Future efforts will focus on sustainment and improving wait times for all patients.

61 Background: Long wait times are a common and occasionally unavoidable experience for patients receiving chemotherapy infusions. It unfavorably impacts patient satisfaction and clinic efficiency. Our primary objective was to evaluate the impact of infusion clinic process changes on patient wait times within a Plan, Do, Study, Act (PSDA) framework. Methods: A multi-disciplinary team, consisting of oncology, nursing, pharmacy, quality improvement, and support staff, met to have brainstorming sessions, make surveys, and conduct time studies to analyze our current process (Plan). A Pareto chart created using survey results showed communication issues were likely causing the largest modifiable impact on our wait times. A common source of miscommunication was whether patients are waiting on labs results for treatment. Beginning March 2019, patients not requiring labs on the day of treatment were assigned to a separate scheduling title to designate them as priority for vital signs and review by pharmacy (Do). Upon clinic check-in, these patients now have vitals signs collected immediately and pharmacy is notified of their arrival to begin chemotherapy preparation. Results: Baseline as well as prospective wait time data was collected from 30 clinic days (360 patient wait times) from chart reviews and timestamp data available in the electronic medical record (EMR). Results were analyzed (Study) using Statistical Process Control (SPC) charts to allow for early detection of improvement. Pre-implementation wait times averaged 1 hour and 37 minutes. Post implementation wait times average 1 hour and 16 minutes (Act). This change was significant based on a shift visible on the SPC chart. Our balancing measure of wait times for all patients did not increase compared to baseline (2 hours and 15 minutes). No significant correlation was observed between average daily wait times and the day of the week or the number of patients treated that day at baseline. Conclusions: Implementation of a new clinic scheduling title and workflow reduced wait times for patients not requiring labs on the day of treatment without increasing wait times for patients requiring labs. Additional PDSA cycles will be conducted for further reductions in wait times for all patients at our site.

176 Background: Rituximab(R) is a novel anti-CD20 monoclonal antibody used in multiple hematologic and rheumatologic diseases. Patients who are hepatitis B surface antigen (HBsAg) or hepatitis B core antibody (HBcAb) positive are at risk for hepatitis-B reactivation with R-based treatments. Therefore, the VA’s medication use evaluation tracker recommends serological testing for HBsAg, HBsAb and HBcAb within 6 months prior to initiating R. We conducted a PDSA cycle to improve Hepatitis-B serological testing in patients receiving R at Birmingham VA medical center (BVAMC). Methods: For baseline evaluation, patients at BVAMC who were treated with R between 2004- march 2014 were retrospectively evaluated for HBV serology results 6 months prior to first treatment. Presence of all 4- HBsAg, HBsAb and HBcAb (IgM and total) was complete; absence of all 4 was ‘not done’ with remaining being incomplete. After conducting a RCA, 3 pharmacy-based interventions were implemented- 1) pharmacy education; 2) pharmacist autonomy to order hepatitis B serology eliminating physician dependence; 3) pharmacy reminders to health care providers to order HBV serology. We aimed to improve ‘complete’ screening rates to > 90% in the post-intervention period (april 2014-sep 2016). Results: In the pre-intervention group (n = 162), majority (81%) had hematologic indications and complete testing was performed in 38%, reminder being incomplete (13%) or not done (49%). Post-intervention (n = 86), majority (54%) had rheumatologic indications with complete testing in 71%, incomplete in 15% and not done in 14%. No Hepatitis B reactivations were identified in either study periods. Conclusions: Pre-intervention, ‘complete’ testing was performed in only one-third of patients and our intervention was effective in almost doubling screening rates. We also demonstrate increasing use of Rituximab in non-hematologic conditions and with second-generation anti-CD20 Ofatumumab and Obinutuzumab also requiring similar testing; our study could standardize Hep-B screening for these agents. A second PDSA cycle with a reflex ‘forcing-function’ EMR-based tool to decrease reliance on human factors is planned to meet the study goal of > 90%.