Health systems interested in improving their patient flow face a number of questions as they design their new way of working. Uncertainty surrounding resource needs and durations of new process steps can lead to ambiguity around whether the new process will actually perform better than the existing one. Providing a way to test the new flow and experiment with different options for each of the variables leads to valuable insight for stakeholders facing these decisions.
Employing simulation modeling at this tactical level to test different scenarios leads to a more informed decision about the best combination of variables in the new patient flow. Modeling the system gives stakeholders a better understanding of how patients will move through the new system and where inefficiencies might arise. The model also allows for experimentation without the risk of using the real system to test options. Using a simulation model to study the proposed patient flow and adjust as necessary leads to a more refined solution that has a better chance of success.
Simulation models do not have to be complex programs that represent every micro-step in a process. They should answer specific questions and provide meaningful results. A model built to answer a utilization question regarding the specific number of check-in resources necessary to successfully run a new registration process in a clinic might only capture the time from patient arrival to patient rooming. Since the rest of the process does not affect the check-in resource, it is not important that it be part of the model. Below, we provide a case study around universal resources in a digestive health clinic.
In this case, three clinics, operating in three separate parts of a hospital, are brought together to operate out of the same shared space. Initially, the stakeholders were concerned about the amount of space in the new location because it did not represent a sum of the three existing footprints. There were also fears about the size of the waiting room. Based on anecdotal information and experienced, we could have tried to explain that the space would be able to support all of the activities that needed to take place, but this would not have alleviated the understandable distress that the stakeholders were feeling.
Instead, Array developed a simulation model to test the throughput of the three clinics and determine whether the space would be able to support them. Real system data drove the model, which provided specific answers to questions around exam room utilization, waiting room volumes and waiting durations. Not only did the simulation provide answers, it gave the users confidence in the design and led to consensus around how the three clinics would interact and share resources once the new space opened.