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Patients Versus Profits
No full textTwo motives drive much of American health care. The first is the motive to heal the sick—the patient motive. The second is the motive to generate financial gains—the profit motive. This Article asks the following question: where do these two motives intersect and diverge? Using three emerging technologies as examples, this Article provides a framework for answering this question. It then uses this framework’s insights to explain when and how legal and extra-legal institutions can be used to improve care, either by constraining the profit motive or aligning it more closely with the goals of patient care. This Article concludes with some thoughts about the prospect of using market mechanisms to improve patient health
Chancery Court Rules that DGCL § 271 Does Not Require Stockholder Approval of Foreclosure Sale of Corporate Assets
No full textChancery Court Rules That Legacy Charter Provision Governing Amendments Did Not Opt Out of Revised DGCL § 242(d)’s Lower Voting Standard for Increasing Authorized Shares
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Forging Alliances: Abortion, Pregnancy Loss, and a Shared Vision for Reproductive Freedom
No full textReconceiving Safety Regulation for AI and ML Medical Software
No full textThis article explores the challenges of regulating AI and ML clinical decision support tools intended to assist trained health care professionals in delivering clinical care. Two old, twentieth-century regulatory models have dominated discussions of medical AI policy since 2013. Thinking inside these old regulatory boxes has not produced effective regulatory solutions to address the novel risks AI poses in clinical care. The first regulatory box treats software as a medical device, which tasks medical device regulators with making software safe but neglects the crucial roles physicians, nurses, administrators, medical practice regulators, and other health oversight bodies must also play to make AI-enabled health care safe for patients. The second box views AI-enabled healthcare as a complex sociotechnical system where the central regulatory challenge is to incentivize the creation of “slack” at the human-AI interface: that is, to inject buffers, redundancies, and checks and balances that enhance opportunities for human actors to intervene if the software runs amok. The 21st Century Cures Act of 2016 favored this latter model, but it has not been successfully implemented. This article then offers two more conceptualizations. Viewed through the lens of the old corporate practice of medicine doctrine, the central oversight challenge in AI-enabled health care is to prevent non-physician corporate actors (for example, software developers) from overriding or corrupting physicians’ ability to exercise independent medical judgment on behalf of their patients. The final conceptualization likens AI and ML tools to intelligent agents that are “colonizing” the health care system and threatening various harms to the indigenous humans—patients, health professionals, and administrators—who inhabit it. Protecting the safety, culture, and values of health care may require institutional and regulatory reforms that go far beyond merely repurposing old regulatory frameworks left over from the past century
The Privacy Law Jigsaw: Piecing Together Legal Compliance When Pieces Don’t Fit
No full textOver the past six years the United States has shifted from a relatively stable and laissez-faire privacy regime, anchored by a few, sector-specific federal statutes such as the Health Insurance Portability and Accountability Act, to an increasingly fragmented landscape dominated by numerous state-level consumer privacy laws. While these laws share commonalities, they also vary in important ways. This Article analyzes that patchwork legal regime and its impact on businesses through four points of friction: (i) statutory applicability and thresholds; (ii) notice-and-choice requirements; (iii) individual data-management rights; and (iv) controller–processor contracting obligations. This Article further explores how modern service-delivery models, especially software-as-a-service and AI tools, undermine the traditional controller—processor dichotomy, leaving the entities that exercise the greatest de facto control over personal data with the fewest direct statutory duties. Concluding that incremental state legislation is unlikely to resolve these flaws, the Authors advocate for a unified privacy framework that would focus regulatory obligations on the nature and risk of the processing rather than arbitrary data categories, thus providing clearer compliance pathways for businesses and stronger privacy protections for individuals
