Health Data Analysis & Advocacy

This course will focus on the role of data in varied settings of health policy, law, and regulation. Topics change year to year and may include: reimbursement levels set by the Centers for Medicare and Medicaid Services; quality analysis for Accountable Care Organizations and other Advanced Payment Models; calculation of medical loss ratios and other critical quantitative analysis for private insurers; quality measures for providers and hospitals; data analytics for public health measures; FDA assessments of drug safety and effectiveness; rankings and ratings of physicians and hospitals; statistical assessment of patterns of fraud and abuse; the role of data in Advanced Payment Models and other "pay for performance" alternatives to fee-for-service; the role of "middlemen" such as pharmacy benefit managers and group purchasing organizations in larger networks of health care finance; sampling cases for False Claims Act liability; proper information about outcomes for informed consent purposes; drug effectiveness and approvals; valuation of procedures via the resource-based relative value scale method; and COVID-19 data disputes. We will consider the role of big data, AI, and machine learning in collecting, analyzing, and using health data. Note that this is not a course in statistical methods, computer programming, or machine learning. Rather, our focus is on the intersection of such methods with the legal system, especially their potential uses and limits. There are a number of contexts where knowledge of the bases and limits of such methods can assist in the development of a case and defense of clients. Successful data mining programs at the Centers for Medicare and Medicaid Services (CMS) provide one model here. By requiring standardized collection of billing data and hiring private contractors to analyze it, CMS has pioneered innovative techniques for discovering fraud. Attorneys can learn from its practices (and the growing secondary literature describing and critiquing them) to develop new skills for deterring illegal conduct. Such awareness becomes increasingly important as cutting-edge analytical methods fuel algorithmic assessments designed to detect legal violations. Evaluation: Will be based on a presentation and a 24-hour final exam. Reading: No textbook; reading will be based on articles assigned in the syllabus.