A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force.

BackgroundTelehealth is the delivery of health care at a distance, using information and communication technology. The major rationales for its introduction have been to decrease costs, improve efficiency and increase access in health care delivery. This systematic review assesses the economic value of one type of telehealth delivery - synchronous or real time video communication - rather than examining a heterogeneous range of delivery modes as has been the case with previous reviews in this area.MethodsA systematic search was undertaken for economic analyses of the clinical use of telehealth, ending in June 2009. Studies with patient outcome data and a non-telehealth comparator were included. Cost analyses, non-comparative studies and those where patient satisfaction was the only health outcome were excluded.Results36 articles met the inclusion criteria. 22(61%) of the studies found telehealth to be less costly than the non-telehealth alternative, 11(31%) found greater costs and 3 (9%) gave the same or mixed results. 23 of the studies took the perspective of the health services, 12 were societal, and one was from the patient perspective. In three studies of telehealth to rural areas, the health services paid more for telehealth, but due to savings in patient travel, the societal perspective demonstrated cost savings. In regard to health outcomes, 12 (33%) of studies found improved health outcomes, 21 (58%) found outcomes were not significantly different, 2(6%) found that telehealth was less effective, and 1 (3%) found outcomes differed according to patient group. The organisational model of care was more important in determining the value of the service than the clinical discipline, the type of technology, or the date of the study.ConclusionDelivery of health services by real time video communication was cost-effective for home care and access to on-call hospital specialists, showed mixed results for rural service delivery, and was not cost-effective for local delivery of services between hospitals and primary care.

epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, a model-based analysis's value lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. These are the hallmarks of good modeling practice.The extent to which an uncertainty analysis can be considered fit for purpose in part depends on the decision(s) the modeling seeks to support. Uncertainty analysis can serve 2 main purposes: assess confidence in a chosen course of action and ascertain the value of collecting additional information to better inform the decision.Many models are designed to help decision makers maximize a given outcome (e.g., cases identified in a screening model, or quality-adjusted life-years, in a cost-effectiveness model), subject, perhaps, to one or more limiting constraints (such as a fixed budget). The model generates point estimates of the outcome for each possible course of action; the ''best'' choice is the one that maximizes the outcome subject to the constraint. If the decision maker has to make a resource allocation decision now, has no role in commissioning or mandating further research, and cannot delay the decision or review it in the future, then the role of uncertainty analysis is limited and the decision should be based only on expected values (although some commentators have argued that for nonlinear models, probabilistic sensitivity analysis [PSA] is required to generate appropriate expected values 7 ). Nevertheless, decision makers may want to gauge confidence in the best choice's appropriateness by exploring its robustness to changes in the model's inputs.Increasingly, models are developed to guide decisions of particular bodies (e.g., organizations responsible for deciding whether to reimburse a new pharmaceutical). Such decision makers who have the authority to delay decisions or review them later, based on research they commission or mandate, should be interested not just in expected cost-effectiveness but also in a thorough uncertainty analysis and the value of additional research. Such information, as well as assessments of factors such as the costs of reversing a decision shown to be suboptimal as further information emerges, and the cost of research and likelihood of undertaking it, can influence the array of decisions available. Thus, uncertainty analysis conveys not only qualitative information about the critical uncertainties surrounding a decision but also quantitative information about the decision maker's priorities in allocating resources to further research.Many models are developed for general dissemination, without a specific decision maker in mind. Such models could inform a range of decision makers with varying responsibilities. Here, there is a case for undertaking a full uncertainty analysis, thus allowing different decisi...