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.
WINNIPEG REGIONAL HEALTH AUTHORITY WINNIPEG, MANITOBA, ONTARIO, CA The Winnipeg Regional Health Authority (WRHA) is one of the largest, most diverse health regions in Canada. It is responsible for providing healthcare to more than 700,000 people living in the City of Winnipeg, as well as the surrounding Rural Municipalities of East and West St. Paul and the Town of Churchill, located in northern Manitoba. The WRHA also provides healthcare support and specialty referral services to nearly 500,000 Manitobans who live outside its boundaries, as well as residents of northwestern Ontario and Nunavut, who require the specialty referral services and expertise available in Winnipeg. http://www.wrha.mb.ca/index.php
PROBLEM In an infusion center, a large group of 20 patients are asked to arrive for the same appointment time. These patients are all led to their bed and wait while eight nurses set up and begin treatment on only eight patients at a time. While a nurse attends to the first patient, the unlucky last person is waiting. In a facility like this, that last patient might wait more than 10 minutes before a provider sets up and begins their treatment. Patients don’t like waiting and health systems try their best to reduce patient waiting time. By asking smaller groups of patients to show up at staggered times, systems can drastically reduce patient waiting time. Array developed a short simulation model to show how systems can achieve these shortened patient wait times without incurring cost. In this simulation, there are two types of patients: 40% fistula (green) and 60% central line (red). Fistula patients have a set-up time between six and 15 minutes. Central line patients have a set-up time between three and 10 minutes. All patients have treatment duration of three to four hours.
Batch
Client Profile
Method Figure 1: Simulation model demonstrating 20 patients arriving at 8:00 am. STRATEGIC DECISION SUPPORT Every day, we make choices. They can be trivial, only affecting a small group; they can also be critical, affecting many aspects of an organization. Array’s trusted advisors can assist with your next strategic decision so you can see the impacts of your options before choosing. Utilizing proven techniques, we provide a range of custom analysis options that focus on your specific need.
BATCH ARRIVALS In the model above, providers lead 20 patients to their beds at exactly 8:00 am. Then eight nurses work to begin an infusion treatment and make sure each patient is comfortable. By 8:26 am, all 20 patients have begun their treatment. Twenty-six minutes to set up 20 patients might seem like a great turn-around time, but it comes at a cost. Since there are only eight providers, 12 people must wait at least a small amount of time before a nurse can attend to them. On average, those 12 people wait for about 10 minutes before a nurse can see them. Additionally, the maximum waiting time for a single patient is about 16 minutes.
Simple models can show the impact of different options, leading to more informed decisions.
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Stagger
Transformation Our core mission is the same as that of our clients: improve the quality of our work, increase our efficiency, and motivate our staff to reach for success. At Array, we are establishing a culture of continuous improvement at all levels of our organization. We seek to empower team members to be agents for good change.
Figure 2: Simulation model demonstrating 5 patients arriving at 8:00 am. STAGGERED ARRIVALS In the model above, patient-arrival times are staggered, with five patients arriving every 10 minutes. The nursing staff is also staggered, with four providers starting at 8:00 am, two more starting at 8:10 am and an additional two starting at 8:30 am. By increasing the nursing staff as patients arrive, fewer patients need to wait to begin their infusion treatment. By 8:38 am, all 20 patients have begun their treatment.
We begin all endeavors with a consideration of process before exploring solutions. Our team can guide your organization through pre-design, ensuring clear goal setting, target outcomes, process analysis, process design and decision support. Our Lean-led approach to project definition provides a clear path to the right project before you begin to design.
While the staggered option completes about 12 minutes after the batch option, the number of patients waiting, and their average waiting time, has drastically decreased. Since the staffing matches the patient arrivals more closely, only three patients must wait before a nurse attends to them. On average, those three people are only waiting about two minutes before being seen, with a maximum waiting time for a single patient of about six minutes.
HOW CAN WE TEST STRATEGIC PLANNING OPTIONS? Our team of experts can help you evaluate the strengths and weaknesses of different strategic planning options. Whether you’re making decisions about department adjacencies or trying to analyze the benefits of hiring an additional surgeon, we provide the tools and talent necessary to make the process easier.
TOOLS USED: Simulation Modeling, Observation, Time Study
Figure 3: Experiment results showing effects of increasing initial nursing capacity on patient waiting time and number of patients waiting in stagger model.
RESULTS Array developed an experiment to test the impact of starting the stagger model with different numbers of nurses. As expected, the results show that as the initial nursing capacity for staggered patients increases, the number of patients waiting, and the duration of the wait decreases. This model is applicable to a number of different situations. A health system could use the results of this experiment to decide how many nurses to begin the day with, and determine whether there could be cost-savings associated with staggered staff starting times. Systems can adjust any of the variables to reflect their individual operating procedures.
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