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We analyze wartime prosthetic device patents to investigate how demand and procurement policy can shape medical innovation. We use machine learning tools to develop new data describing the aspects of medical and mechanical innovations that are emphasized in patent documents. Our analysis of historical patents yields three primary facts. First, we find that the U.S. Civil War and World War I led to substantial increases in the quantity of prosthetic device patenting relative to patenting in other medical and mechanical technology classes. Second, we find that the Civil War led inventors to focus broadly on improving aspects of the production process, while World War I did not, consistent with the United States applying a more cost-conscious procurement model during the Civil War. Third, we find that inventors emphasized dimensions of product quality (e.g., a prosthetic's appearance or comfort) that aligned with differences in buyers' preferences, as described in the historical record, across wars. We conclude that procurement environments can significantly shape the scientific problems with which inventors engage, including the choice to innovate on quality or cost.

How does FDA regulation affect innovation and market concentration? I examine this question by exploiting FDA deregulation events that affected certain medical device types but not others. I use text analysis to gather comprehensive data on medical device innovation, device safety, firm entry, prices, and regulatory changes. My analysis of these data yields three core results. First, these deregulation events significantly increase the quantity and quality of new technologies in affected medical device types relative to control groups. These increases are particularly strong among small and inexperienced firms. Second, these events increase firm entry and lower the prices of medical procedures that use affected medical device types. Third, the rates of serious injuries and deaths attributable to defective devices do not increase measurably after these events. Perhaps counterintuitively, deregulating certain device types lowers adverse event rates significantly, consistent with firms increasing their emphasis on product safety as deregulation exposes them to more litigation.

Background Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. Objective 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. Methods 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. Results 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. Conclusions 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.

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