Data science for high-stakes economics, litigation and healthcare decision making
Data science for high-stakes economics, litigation and healthcare decision making


Advanced data science and machine learning increasingly penetrate all aspects of our lives, from our consumption habits and exposure to tailored ads to the analysis of our DNA and the development of new drugs. This session will explore data science applications in economics, litigation and life sciences scenarios where high stakes outcomes can hinge on a single analysis and time is limited. From eye-tracking surveys used to determine the value of patented smartphone features to evaluations of drug effects that can make or break market access, decision makers and regulators across industries are leveraging data science tools in game changing ways. Natural language processing, for example, now allows economists to incorporate extensive text data into analyses of patent valuations, financial event studies or merger analyses. Supervised machine learning algorithms are similarly frequently used for applications such as stock-picking or fraud detection and monitoring. In the life sciences, data science tools are expanding access to data and analytics, and driving insights that improve patient care and enable drug development. Interactive analytics and data visualizations are also helping to engage patients and to communicate health impacts more effectively to broader groups of decision makers. In Health Economics and Outcomes Research, language processing algorithms are used to prioritize the review of studies or integrate doctors’ notes into analyses. Technological advances in the speed and cost efficiency of genome sequencing further open the door for tailored medicine, including drug resistance prediction at the individual level. This session will discuss these applications, including practical challenges and solutions, and the role of organizational factors in maximizing the value of data science and its impact on key outcomes.


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