title: Using prediction models and cost-effectiveness analysis to improve clinical decisions: emergency department patients with acute chest pain.
abstract: Prediction models and cost-effectiveness analysis are two of the methodologies included in the broad definition of outcomes research. These methodologies are designed to improve physicians' abilities to identify clinical risks and to choose appropriate management strategies based on these risks. For the evaluation and management of patients with acute chest pain, prediction models have markedly improved our ability to estimate risk, and cost-effectiveness analyses have helped guide the development of new paradigms and the incorporation of new technologies. In the past decade, the management of patients who come to emergency departments with acute chest pain has fundamentally changed, with far fewer patients being admitted to coronary intensive care units and an increasing majority being admitted to nonintensive, observation units for shorter and shorter periods of time. These changes in management approaches actually allow more patients to be admitted, hence reducing the risk of inappropriate discharge, while still reducing the utilization of resources.
authors.affiliation: Department of Medicine, University of California, San Francisco School of Medicine 94143-0120, USA.
authors.forename: L
authors.initials: L
authors.lastname: Goldman
mesh_terms: D002637:Chest Pain / Q000628:therapy*; D003362:Cost-Benefit Analysis; D003657:Decision Making; D004636:Emergency Service, Hospital / Q000191:economics*; D006801:Humans; D008962:Models, Theoretical; D009203:Myocardial Infarction / Q000175:diagnosis; D017063:Outcome Assessment, Health Care; D011336:Probability; D014218:Triage / Q000191:economics