Cancer clinic redesign: Opportunities for Resource Optimization

• DOI: 10.20944/preprints202112.0306.v2
• Publication Date: 2022-04-27T08:16:44Z
• Authors: Michael Fung-Kee-...; Rachel S. Ozer; Bill Davies; Stephanie Pick; Kate Duke; David J. Stewart; M. Neil Reaume; Marcus Ward; Katelyn Balchin; Robert M. MacRae; Shannon Nelson; Julie Renaud; Dennis Garvin; Suzanne Madore; Jason R. Pantarotto
• Keywords: learning health system; ambulatory clinic; block schedule; disease site teams; interdisciplinary care; cancer operations; oncology value stream; interdisciplinary care; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; disease site teams; block schedule; Ambulatory Care Facilities; Article; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; learning health system; Neoplasms; cancer operations; Humans; ambulatory clinic; RC254-282; oncology_oncogenics

Abstract: Ambulatory cancer centers face fluctuating patient demand and deploy specialized personnel who have variable availability. This undermines operational stability through misa-lignment of resources to patient needs, resulting in overscheduled clinics, budget deficits, and wait times exceeding provincial targets. We describe deployment of a Learning Health System framework for operational improvements within the entire ambulatory center. Known methods of value stream mapping, operations research and statistical process control were applied to achieve organizational high performance that is data-informed, agile and adaptive. We transitioned from a fixed template model by individual physician to a caseload management by disease site model that is realigned quarterly. We adapted a block schedule model for the ambulatory oncology clinic to align the regional demand for specialized services with optimized human and physical resources. We demonstrated improved utilization of clinical space, increased weekly consistency and im-proved distribution of activity across the workweek. Increased value, represented as the ratio of monthly encounters per nursing worked hours, and increased percentage of services delivered by full-time nurses were benefits realized in our cancer system. The creation of a data-informed demand capacity model enables application of predictive analytics and business intelligence tools that will further enhance clinical responsiveness..