title: Accuracy of prediction models in the context of disease management.
abstract: There has been a significantly increased interest in the adoption of prediction modeling by many disease and case management programs to risk stratify members in order to optimize the utilization of available clinical resources. Before adopting any prediction model, it is critical to understand how to evaluate the model's accuracy. This paper explains the basic concepts of prediction accuracy, the relevant parameters, their drawbacks, and their interpretations. It also introduces a new accuracy parameter termed "cost concentration," which indicates the model accuracy more explicitly in the context of disease management.
authors.affiliation: BioSignia Inc., 1822 E. NC Highway 54, Suite 350, Durham, NC 27713, USA. ghu@biosignia.co
authors.forename: Guizhou
authors.initials: G
authors.lastname: Hu
authors.forename: Martin
authors.initials: M
authors.lastname: Root
mesh_terms: D019468:Disease Management; D006801:Humans; D007345:Insurance Claim Review; D008329:Managed Care Programs; D017059:Models, Econometric; D017146:Models, Organizational; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D020379:Risk Adjustment / Q000379:methods*; D012680:Sensitivity and Specificity