Cancer Clinic Redesign: Opportunities for Resource Optimization

Abstract

Ambulatory cancer centers face a fluctuating patient demand and deploy specialized personnel who have variable availability. This undermines operational stability through the misalignment of resources to patient needs, resulting in overscheduled clinics, budget deficits, and wait times exceeding provincial targets. We describe the 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 an 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 an improved utilization of clinical space, increased weekly consistency and improved distribution of activity across the workweek. The increased value, represented as the ratio of monthly encounters per nursing worked hours, and the 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 the application of predictive analytics and business intelligence tools that will further enhance clinical responsiveness.

Keywords: ambulatory clinic; block schedule; cancer operations; disease site teams; interdisciplinary care; learning health system; oncology value stream.

Figure 1

The total weekly activity in the six clinic modules during the three sample intervals (Pre, Post, Pandemic) became less variable and underutilization was reduced. The large activity disruption in each interval is the New Year period. Activity in Mod E was increased from 4% to 17% and in Mod C from 7% to 14% while the fraction in Mod B was reduced from 20% to 15%. Two chi-square tests of independence showed that there was a significant and sustained association between the schedule change and activity distribution in the modules, (Pre-Post X² (6, N = 25,293) = 4110, p < 0.0001, Pre-Pandemic X² (6, N = 43,431) = 8329, p < 0.0001).

Figure 2

Weekly variability in daily encounters was reduced as well as variability between weekdays. The encounters on each weekday during the three evaluation periods are summarized in box plots (A) Pre, (B), Post and (C), Pandemic. Sustained reduction of variance for Mon, Tues & Wed was observed (*) (F Test Pre-Post and Pre-Pandemic Mon p < 0.0001, p < 0.0001, Tues p < 0.0001, p < 0.0003, Wed p < 0.0001, p < 0.03). The change on Monday demonstrated by reduction of the coefficient of variation (Standard deviation/mean) from 0.48 to 0.12 (smaller box) and variability decreased on Tuesday and Wednesday due to reduction of low utilization outliers, (smaller bottom whisker). Friday and Monday activity level increased while Tuesdays decreased as proportion of weekly total (flatter row of boxes). Two chi-square tests of independence showed that there was a significant and sustained association between the schedule change and activity distribution between the weekdays, (Pre-Post X² (4, N = 25,293) = 94, p < 0.0001, Pre-Pandemic X² (4, N = 43,431) = 557, p < 0.0001).

Figure 3

Value (Encounters delivered/Nursing worked hours) was increased 1.17-fold, (*) (z test, p < 0.02) (A) and the percentage of nursing hours from full-time nurses increased from 52% to 55% (*) (z test, p < 0.0001) (B). The total monthly encounters in each evaluation period were divided by monthly worked hours to describe value and the fraction of monthly nursing worked hours from full time nurses.