CASE STUDY
CLIENT: Winnipeg Regional Health Authority Winnipeg, Manitoba, Ontario, CA
Simulation Model Study: Reducing Patient Arrival Batch Size Decreases Patient Waiting Time While a Canadian public health system was in the early planning stages for a new regional dialysis infusion center, they wanted to remove inefficiencies from their patient arrival and hook-up process. Unable to grasp the impact patient arrival schedules had on infusion hook-up wait times, they desired a quick way to evaluate the differences before making a decision. SERVICE: Transformation
EXECUTIVE SUMMARY
METHOD: Strategic Decision Support
In Canada, healthcare is a public service provided to all citizens. As the cost of health services increases, administration looked for ways to reduce inefficiencies. An infusion center in Winnipeg was interested to see whether smaller arrival groups
Challenge Dialysis center staff were unsure of how a change to their patient arrival process might impact staffing and wait times. Early in the project, they needed to decide how they might adjust their process and desired evidence to support their selection.
Solution Array was able to quickly model this system’s problem using discrete event simulation. The model was designed to answer a simple question, “How do patient arrival patterns impact patient waiting times?” We designed the model using high-level patient arrival data and combined different arrival strategies with various nursing options. The simulation results provided the data necessary for the team to make a quick decision.
could improve dialysis patient hook-up efficiency. Array developed a simulation model to quickly demonstrate the efficiency difference between two patient arrival patterns. The simulation took into account two patient types, each with different set up durations. Across both patient types, dialysis infusion durations can last between three and four hours. Patients arrive to the center, go to an open infusion bay where a nurse hooks them up, and remain until their treatment is complete, at which point they leave the facility. We presented two scenarios to our client, which demonstrate the difference between staggering 20 patient arrivals by groups of five over 20 minutes and batching 20 patients in one arrival group. For each patient in the different scenarios, we measured the length of time from arrival to the start of set up. The model demonstrated a significant reduction in patient waiting time between arrival and hook-up when staggering arrivals by groups of five. Additionally, we presented an option to stagger nursing shift arrivals to help minimize the cost of staffing and align staffing with patient volumes. By visualizing the possible scenarios, we were able to provide concrete evidence of potential efficiency gains.