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

• Published in: Journal of medical Internet research Vol. 25; no. 1; p. e43486
• Main Authors: Rogers, Parker, Boussina, Aaron E, Shashikumar, Supreeth P, Wardi, Gabriel, Longhurst, Christopher A, Nemati, Shamim
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 13.02.2023; JMIR Publications
• Subjects: Aged; Alarms; Algorithms; Classification; Comorbidity; Compliance; Cost control; Cost estimates; Data collection; Deployment; Economic factors; Efficacy; Health care expenditures; Humans; Medicaid; Medical diagnosis; Medicare; Multimedia; Noncompliance; Optimization; Original Paper; Patients; Policy making; Prediction models; Predictive analytics; Prospective payment systems; Review boards; Sepsis; Sepsis - diagnosis; Sepsis - therapy; Tolerance; Treatment Outcome; United States; United States; United States > US; California; workflow simulation; cost; data; simulation; implementation; diagnosis; technology; machine learning; evaluation; clinical; utility assessment; acute kidney injury; model; injury; sepsis; care
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/43486

Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. The aim of this study was to 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. We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) 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 such as noncompliance, treatment efficacy, and tolerance for false alarms on the net benefit of triggering sepsis alerts. Compliance 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 US $4.6 billion in excess cost savings for CMS. 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. We provide a framework that CMS policymakers could use to recommend minimum adherence rates to the early recognition and appropriate care of sepsis that is sensitive to hospital department-level incidence rates and national excess costs. Customizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.