Hospitals and healthcare systems are complex. Various factors can affect how they perform and how well patients are taken care of. It can be a challenge to handle all these different factors, understand their impact on departments and processes, and make improvements without the right tools.
Healthcare simulation provides detailed analysis with valuable insights. Experiments with simulation models can help:
- Identify the problems down to individual procedural areas, beds, staff utilization, etc.
- Test changes and measure their impact on waiting times, staff scheduling, emergency departments, and patient flows.
- Strategically plan changes and improvements in healthcare based on the analysis of test results.
With simulation, you can efficiently design hospitals, plan resource utilization and staff scheduling, avert emergency room overcrowding, and optimize pharmaceutical supply chains. It is also possible to develop disease mitigation strategies as, for example, it was done during COVID-19 for evaluating disease spread and mass vaccination.
Let’s have a closer look at healthcare-related challenges that you can resolve with simulation backed by some real-world examples.
Hospital design and planning for better operations management
Simulation engineers can design new hospital wards and plan resources, capacity, and work methods that might be used there. Here’s a simplified example of a multi-story hospital building where automated guided vehicles transport meal, laundry, and waste from the ground to the upper floors.
When designing a hospital, one of the main aspects to consider is the waiting room and the patient flow. Sometimes new restrictions, such as space and people policies during COVID-19, are introduced, and that is when you might need to test their implementation in a real waiting room.
Healthcare resource utilization: bed management, emergency room overcrowding, and staff scheduling
Bed management solutions
Efficient and timely hospital resource utilization is vital. It helps avert bed shortages during peak demand, ensures staff availability to deliver high-quality care for patients, and ultimately saves hospitals hundreds of thousands of dollars.
Data-driven healthcare resource utilization simulation helps test different configurations of a hospital through what-if scenarios. This research uses predictive simulation modeling to evaluate bed requirements for both elective and non-elective admission patients so that the hospital management is prepared to deal with the challenges.
A hospital in Sweden also benefited from simulation modeling insights as the management didn’t only test patient allocation scenarios but also estimated the quality of the provided care. This lets them understand whether the hospital service level was acceptable. Hospital staff scheduling
Hospital staff scheduling
Modeling can improve doctors’ scheduling and manage patients in hospitals. One of our clients, Indiana University Health Arnett Hospital (US), wanted to develop a scheduling methodology that would benefit the doctors and patients of the outpatient clinic. The model helped doctors test different working schedules and see how they affected the clinic’s operations.
Another simulation model of a hospital with a specialized express care unit and an emergency department area was developed. It can help analyze facility improvement options, such as staff levels and schedules, express care hours of operation, room utilization rates, etc.
Emergency room overcrowding solutions
One of the clients in Saudi Arabia used simulation to find a solution to the the patient overcrowding problem in hospitals' emergency rooms. The model developers wanted to understand how to create a smooth patient flow and decrease the length of the patient stay. The outputs helped identify the emergency rooms’ full capacity and better utilize human resources to prevent overcrowding.
Also, AnyLogic simulation modeling software allows the development of detailed models for healthcare decision support and measuring the impact of adopting new technologies, such as mobile stroke units.
Epidemic simulation: predicting and preventing disease transmission
Simulation of epidemic trends is also possible in AnyLogic software. Simulation modeling results can help inform decision-making regarding the spread and effects of viruses and diseases. For example, to better understand the transmission of a new coronavirus among the population and the effects of control measures, an agent-based model was developed at the outset of the COVID-19 outbreak. It helped predict how quickly this coronavirus could spread through a Chinese population and take measures to prevent it.
Epidemic models are important for deploying and coordinating further prevention and control. Hybrid dynamic models provide insight and decision support when applied to challenges such as the COVID-19 outbreak. Here’s an agent-based epidemic model that evaluates the impact of non-pharmaceutical interventions on the COVID-19 virus spread. In addition, agent-based epidemic simulation can show the dynamics of tuberculosis transmission.
To simulate ongoing processes in a drive-through mass vaccination clinic, an experimental model was created using discrete-event and agent-based modeling methods. The model was eventually used by public authorities in Canada to organize local mass vaccination and prevent the spread of COVID-19.
Using agent-based simulation, engineers developed a predictive healthcare model for Pfizer and simulated personalized treatment processes with great precision. Thanks to modeling, doctors could make informed decisions on drug dosage for a patient and observe a treatment effect.
Pharmaceutical supply chains, production planning, and marketing
Simulation is also useful in pharmaceutical supply chains and production planning. For example, a vaccine manufacturer could determine an optimal vaccine supply chain design from the standpoint of costs and service level.
An agent-based pharmaceutical marketing model for a drug manufacturer indicated for how much time a direct-to-consumer marketing campaign would be effective and helped save the company's budget.
We have examined various cases of how simulation can help resolve healthcare challenges. Each simulation model built in AnyLogic accomplished important objectives: facilitated efficient hospital design, improved resource utilization, optimized patient flows to reduce overcrowding in emergency rooms, or helped better understand epidemic trends. These simulations could significantly contribute to hospital operations management and healthcare systems.
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