CLIENT:
Winnipeg Regional Health Authority CASE STUDY
Winnipeg, Manitoba, Canada
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.
CHALLENGE
EXECUTIVE SUMMARY
Dialysis center staff were unsure of how
In Canada, healthcare is a public service provided to all citizens. As the cost of
a change to their patient arrival process
health services increases, administration looked for ways to reduce inefficiencies.
might impact staffing and wait times.
An infusion center in Winnipeg was interested to see whether smaller arrival groups
Early in the project, they needed to decide
could improve dialysis patient hook-up efficiency. Array developed a simulation model
how they might adjust their process
to quickly demonstrate the efficiency difference between two patient arrival patterns.
and desired evidence to support their
The simulation took into account two patient types, each with different set up
selection.
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
SOLUTION
they leave the facility.
Strategic Decision Support
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
Array was able to quickly model this
patients in one arrival group. For each patient in the different scenarios, we measured
system’s problem using discrete event
the length of time from arrival to the start of set up.
simulation. The model was designed to answer a simple question, “How do
The model demonstrated a significant reduction in patient waiting time between
patient arrival patterns impact patient
arrival and hook-up when staggering arrivals by groups of five. Additionally, we
waiting times?” We designed the model
presented an option to stagger nursing shift arrivals to help minimize the cost of
using high-level patient arrival data and
staffing and align staffing with patient volumes. By visualizing the possible scenarios,
combined different arrival strategies with
we were able to provide concrete evidence of potential efficiency gains.
various nursing options. The simulation results provided the data necessary for the team to make a quick decision.
PROJECT HIGHLIGHTS
4
Staffing Options
9
Fewer People Waiting
10 min. Wait Time Reducation