Analysis Group Team Describes Practical Uses of Machine-Learning Capabilities in Health Care Litigation
September 29, 2016
Machine learning – which draws on insights from a variety of disciplines including computer science, mathematics, and engineering – can efficiently turn enormous volumes of both structured (coded) and unstructured (narrative) data into new capabilities in health care litigation. In a recent Law360 article, Analysis Group Vice Presidents Lisa Pinheiro and Jimmy Royer, Economist Nick Dadson, and Managing Principal Paul Greenberg provided an overview of how machine learning works, and examined the wide array of potential uses of machine-learning algorithms in the health care litigation context.
In this follow-up article, authored by Pinheiro, Royer, Greenberg, and Vice President Mihran Yenikomshian, also appearing in Law360, the Analysis Group team elaborates on practical applications of machine learning in terms of informing legal strategy, identifying relevant materials for experts, and enhancing expert testimony. Such capabilities, when combined with traditional methods, will enable testifying experts to better and more persuasively bolster their opinions. For non-testifying experts, these same tools will enhance their existing value in helping attorneys develop their strategies and conduct informed fact discovery.