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Health Law and Policy

University of Michigan Law School

2017

Data

Articles 1 - 3 of 3

Full-Text Articles in Law

Risk And Resilience In Health Data Infrastructure, W. Nicholson Price Ii Dec 2017

Risk And Resilience In Health Data Infrastructure, W. Nicholson Price Ii

Articles

Today’s health system runs on data. However, for a system that generates and requires so much data, the health care system is surprisingly bad at maintaining, connecting, and using those data. In the easy cases of coordinated care and stationary patients, the system works—sometimes. But when care is fragmented, fragmented data often result. Fragmented data create risks both to individual patients and to the system. For patients, fragmentation creates risks in care based on incomplete or incorrect information, and may also lead to privacy risks from a patched together system. For the system, data fragmentation hinders efforts to improve efficiency …


Regulating Black-Box Medicine, W. Nicholson Price Ii Dec 2017

Regulating Black-Box Medicine, W. Nicholson Price Ii

Michigan Law Review

Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they …


Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii Nov 2017

Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii

Articles

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques—terms I will explain momentarily—innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from healthcare data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including …