Computer disease simulation models: integrating evidence for health policy

Abstract

Computer disease simulation models are increasingly being used to evaluate and inform health care decisions across medical disciplines. The aim of researchers who develop these models is to integrate and synthesize short-term outcomes and results from multiple sources to predict the long-term clinical outcomes and costs of different health care strategies. Policy makers, in turn, can use the predictions generated by disease models together with other evidence to make decisions related to health care practices and resource utilization. Models are particularly useful when the existing evidence does not yield obvious answers or does not provide answers to the questions of greatest interest, such as questions about the relative cost-effectiveness of different practices. This review focuses on models used to inform decisions about imaging technology, discussing the role of disease models for health policy development and providing a foundation for understanding the basic principles of disease modeling. This manuscript draws from the collective computed tomographic colonography modeling experience, reviewing 10 published investigations of the clinical effectiveness and cost-effectiveness of computed tomographic colonography relative to colonoscopy. The discussion focuses on implications of different modeling assumptions and difficulties that may be encountered when evaluating the quality of models. This underscores the importance of forging stronger collaborations between researchers who develop disease models and radiologists, to ensure that policy-level models accurately represent the experience of everyday clinical practices.