OBJECTIVES

: To determine the resource allocation required to deliver a robust sustainable service and demonstrate resource utilisation impact.

METHODS

: A bespoke discrete event simulation model was utilised using Lean Six Sigma methodology for efficiency/inefficiency determination. Principles of pathway mapping, time, staffing, and resource requirements were identified. Simulations were run using a game-based learning approach to determine the impact of changes.

Parameters assessed were patient numbers (350, 400, 450), infusion chairs (5, 6, 7), number of nurses (2, 3, 4), hours (6.5, 7, 7.5) and days open (3, 4, 5). Simulations were run for a 12-month period.

A ratio of annual total time required to deliver service vs infusion chair capacity time was used to assess delivery of service (1 being optimal). Delays to appointments (>3 days) were also determined.

RESULTS

: A total of 243 simulations were performed. Ratios calculated against patient volumes were 0.38-1.19 (350pts), 0.44–1.36 (400 pts) and 0.49–1.53 (450 pts). Delays to appointments were assessed as 0-10.8%, 0-13.9% and 0-16.9% for 350, 400 and 450 patients respectively.

Direct nurse/patient resource utilisation ranged from 14–61% (350 pts), 16–62% (400 pts) and 18–67% (450 pts) across the scenario’s modelled with chair utilisation ranges being 32–83%, 37–91% and 42–91% for the corresponding patient cohorts.

Optimal deployment of resources was determined to be 3 nurses, 5 chairs, 7 hrs/day and 3 days/week for 350 pts and 2 nurses, 5 chairs, 6 hrs/day and 4 days/week for 400 pts. Either 2 nurses, 5 chairs, 7hrs/day and 4 days/week or 2 nurses, 6 chairs, 6hrs/day for 4 days/week was optimal for 450pts.

CONCLUSIONS

: Both macro environmental factors (COVID) and/or micro factors (staffing and funding) may impact on service delivery. Appropriate resource allocation can overcome challenges and allow for continued administration for suitable patients. Further work is required to understand resource requirements to reduce waiting lists.