Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: A Sepsis Case Study

Abstract

ObjectiveTo optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility.Materials and MethodsWe calculated the excess costs of sepsis by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities. We optimized the parameters of a sepsis prediction algorithm across different diagnostic categories to minimize these excess costs. At the optima, we evaluated diagnostic odds ratios and analyzed the impact of compliance factors—like non-compliance, treatment efficacy, and tolerance for false alarms—on the net benefit of triggering sepsis alerts.ResultsCompliance factors significantly contributed to the net benefit of triggering a sepsis alert. However, a customized deployment policy can achieve a significantly higher diagnostic odds ratio and reduced costs of sepsis care. Implementing our optimization routine with powerful predictive models could result in $4.6 billion in excess cost savings for the Medicare program.DiscussionSepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. We designed a framework for customizing sepsis alert protocols within different diagnostic categories to minimize excess costs and analyzed model performance as a function of false alarm tolerance and compliance with model recommendations.ConclusionCustomizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.