Routine measurement of FFR in patients with multivessel coronary artery disease who are undergoing PCI with drug-eluting stents significantly reduces the rate of the composite end point of death, nonfatal myocardial infarction, and repeat revascularization at 1 year. (ClinicalTrials.gov number, NCT00267774.)

Models--mathematical frameworks that facilitate estimation of the consequences of health care decisions--have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.

Routine measurement of FFR in patients with multivessel CAD undergoing PCI with drug-eluting stents significantly reduces mortality and myocardial infarction at 2 years when compared with standard angiography-guided PCI. (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation [FAME]; NCT00267774).

State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.

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The objective of this systematic review is to analyse the relative risk reduction on medication error and adverse drug events (ADE) by computerized physician order entry systems (CPOE). We included controlled field studies and pretest-posttest studies, evaluating all types of CPOE systems, drugs and clinical settings. We present the results in evidence tables, calculate the risk ratio with 95% confidence interval and perform subgroup analyses for categorical factors, such as the level of care, patient group, type of drug, type of system, functionality of the system, comparison group type, study design, and the method for detecting errors. Of the 25 studies that analysed the effects on the medication error rate, 23 showed a significant relative risk reduction of 13% to 99%. Six of the nine studies that analysed the effects on potential ADEs showed a significant relative risk reduction of 35% to 98%. Four of the seven studies that analysed the effect on ADEs showed a significant relative risk reduction of 30% to 84%. Reporting quality and study quality was often insufficient to exclude major sources of bias. Studies on home-grown systems, studies comparing electronic prescribing to handwriting prescribing, and studies using manual chart review to detect errors seem to show a higher relative risk reduction than other studies. Concluding, it seems that electronic prescribing can reduce the risk for medication errors and ADE. However, studies differ substantially in their setting, design, quality, and results. To further improve the evidence-base of health informatics, more randomized controlled trials (RCTs) are needed, especially to cover a wider range of clinical and geographic settings. In addition, reporting quality of health informatics evaluation studies has to be substantially improved.

Health technology assessment (HTA) is a dynamic, rapidly evolving process, embracing different types of assessments that inform real-world decisions about the value (i.e., benefits, risks, and costs) of new and existing technologies. Historically, most HTA agencies have focused on producing high quality assessment reports that can be used by a range of decision makers. However, increasingly organizations are undertaking or commissioning HTAs to inform a particular resource allocation decision, such as listing a drug on a national or local formulary, defining the range of coverage under insurance plans, or issuing mandatory guidance on the use of health technologies in a particular healthcare system. A set of fifteen principles that can be used in assessing existing or establishing new HTA activities is proposed, providing examples from existing HTA programs. The principal focus is on those HTA activities that are linked to, or include, a particular resource allocation decision. In these HTAs, the consideration of both costs and benefits, in an economic evaluation, is critical. It is also important to consider the link between the HTA and the decision that will follow. The principles are organized into four sections: (i) "Structure" of HTA programs; (ii) "Methods" of HTA; (iii) "Processes for Conduct" of HTA; and (iv) "Use of HTAs in Decision Making."

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