Improving Clinic Flow at an Academic, Safety-Net, Surgical Oncology Clinic

Date

2019-04-01

Authors

Tran, Matthew

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BACKGROUND: A high-volume, academic, safety-net, surgical oncology ambulatory clinic sees patients twice a week. As healthcare systems move towards pay-for-performance, maximum workflow and efficiency become critical to both patient access to care and experience. LOCAL PROBLEM: The clinic has inefficiencies causing excessive delays leading to high patient dwell times, which negatively affect patient and provider satisfaction. The purpose of this study is to use quality improvement tools to decrease these wait times. METHODS: Quality improvement tools, lean, and DMAIC (define, measure, analyze, improve, control) methodology was used to guide the project. The baseline for the clinic was established with patient dwell times, defined as patient check-in to check-out in the Epic system. A value stream map was created to identify value-add and non-value-add steps. Time studies, interviews, and Pareto charts were designed to assess top non-value-add times. Root cause analysis with a fishbone diagram was used to identify areas of opportunity for interventions. A prioritization matrix was generated to evaluate the most effective solutions, and the interventions were chosen after discussion with clinic staff. After their implementation, data were collected prospectively: Epic tracked dwell time data, and Press Ganey gathered patient satisfaction scores. The datasets before (March 2016 - March 2017) and after (April 2017 - April 2018) the intervention was compared using statistical analysis including t-tests and control charts. INTERVENTIONS: Two interventions were chosen: (1) Patients were pre-assigned to residents before clinic start time to reduce the time they spent reviewing the patient chart before the patient visit. (2) A centralized supply cart was introduced to improve clinic flow for procedures. RESULTS: During the pre-intervention period from March 2016 to March 2017, the Press Ganey survey reported a patient satisfaction score of 87 (n=27). This score is about two standard deviations below the benchmark of 93 (n=1,243). During the post-intervention period of April 2017 - April 2018, the Press Ganey score increased to 88 (n=23), but the response rate was <1%. During the pre-intervention, the mean dwell time in the clinic was 140.67 minutes (n=572) and 123.02 minutes (n=2,802) for new and follow-up patients, respectively. The post-intervention mean dwell times in the clinic were 117.35 minutes (n=589) and 110.64 minutes (n=2,137) for new and follow-up patients, or about a 17% and 10% reduction respectively. The reductions in dwell time were statistically significant with a p-value of <0.001. The control chart also revealed a special cause variation due to the intervention, which represented a trend of decreasing dwell times for patients. CONCLUSION: Quality improvement tools can be successfully used in this specific setting to streamline clinic flow and improve efficiency to reduce patient dwell times. The next steps are to continue collecting more robust data and iteratively refining the interventions. As the clinic continues to evolve other interventions will be considered for implementation. The success of these solutions can transfer to other clinics in the academic hospital.

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