Interactive Cost-benefit Analysis: Providing Real-World Financial Context to Predictive Analytics

Objective: Clinical implementation of predictive analytics that assess risk of high-cost outcomes are presumed to save money because they help focus interventions designed to avert those outcomes on a subset patients who are most likely to benefit from the intervention. This premise may not always be true. A cost-benefit analysis is necessary to show if a strategy of applying the predictive algorithm is truly favorable to alternative strategies. Methods: We designed and implemented an interactive web-based cost-benefit calculator, enabling specification of accuracy parameters for the predictive model and other clinical and financial factors related to the occurrence of an undesirable outcome. We use the web tool, populated with real-world data to illustrate a cost-benefit analysis of a strategy of applying predictive analytics to select a cohort of high-risk patients to receive interventions to avert readmissions for Congestive Heart Failure (CHF). Results: Application of predictive analytics in clinical care may not always be a cost-saving strategy compared with intervening on all patients. Improving the accuracy of a predictive model may lower costs, but other factors such as the prevalence and cost of the outcome, and the cost and effectiveness of the intervention designed to avert the outcome may be more influential in determining the favored strategy. Conclusion: An interactive cost-benefit analyses provides insights regarding the financial implications of a clinical strategy that implements predictive analytics.