Disease Management Simulation Modeling

Simulation is a general term describing a method that imitates or mimics a real system using a model of that system. Models vary widely and may be a physical object, such as a mannequin used in training healthcare providers, or a conceptual object, such as a supply-demand curve in medical economics. This entry is confined to computer models that are based on a logical or mathematical/statistical structure and use the computer to examine a model's behavior. Models can represent various types of healthcare systems that are engaged in disease management of patients, allowing, for example, examination and comparisons of alternative clinical decisions for patient care, insurance coverage policies, or the processes for delivering safe, effective, and efficient preventive or therapeutic care.

The best practices for disease management use evidence-based medicine, such as the outcomes from observational and experimental human studies, including clinical trials. However, such studies are not always possible and may be impractical. Consider, for example, studies that seek to determine the most effective (balancing risks and benefits) and cost-effective (balancing costs and effectiveness) strategies for colon cancer screening. An effective strategy might include ignoring small polyps in low-risk people, but a prospective human study that includes such a component might never be approved. Determining the best age to initially screen for colon cancer would require an experiment that tested perhaps 25 different ages. Determining the frequency and type of follow-up testing based on a person's family history, biological and social profile, and past test results, including the size and type of past polyps, would require such a large study over such a long period of time as to be essentially impossible. In engineering and in the physical sciences, computational models have been frequently used to complement and to substitute for direct experimentation.