Despite the routine assessment of decisional capacity in the clinical context, the concept of capacity is contested, and its measurement is flawed. For example, scholars and clinicians disagree about the criteria for demonstrating capacity. There is further lack of consensus about whether some types of medical decisions should require more evidence of capacity. Moreover, there is robust literature demonstrating that formal capacity assessment instruments are not always valid and reliable.

This Article focuses on these and other issues, namely the fact that (in)capacity as currently understood is often not viewed relationally and contextually. (In)capacity is viewed as a cognitive property of an individual — an individual has capacity or does not. However, properly conceived, (in)capacity is constituted in interaction with others (e.g., health care providers, family, friends) and one’s environment, and mediated through one’s body and experience of health, illness, or disability. When this reality is not acknowledged, clinicians, caregivers, family members, and others miss opportunities to strengthen patients’ decisional capacity by modifying how they communicate with patients and by changing patients’ environment to improve decision-making. In fact, not acknowledging the relational and contextual nature of (in)capacity likely leads to weakening of patients’ decision-making abilities and the unnecessary disqualification of patients from contemporaneous decision-making, which subsequently negatively affects patient interests in maintaining autonomy and bodily integrity and experiencing wellbeing.

This Article will foreground the relational and contextual nature of (in)capacity and address problems with capacity assessments for adult patients in the medical decision-making context, propose reforms to the capacity assessment process, and conclude by discussing the implications of these arguments in other decision-making contexts.

The use of AvertD, a genetic test to assess an individual’s risk of developing opioid use disorder, will expose physicians to liability. Critics of the test argue that the test itself is unreliable, often resulting in false positives, which will lead to potential undertreatment of pain, and false negatives, which will lead to inappropriate opioid prescriptions. But if the test is available, physicians will use it. And often, due to a combination of genetic determinism and genetic illiteracy, physicians will rely on the test to make opioid prescription decisions without also looking to the environmental, socioeconomic, lifestyle, and other factors that contribute to an individual’s opioid use disorder risk.

Genetic test results that purport to predict opioid use disorder — or really, any behavioral or psychiatric trait or diagnosis — may have enormous consequences for individual lives. When patients are harmed by physicians’ negligent clinical decision making, they can often turn to tort law for a remedy. This article covers various physician liability scenarios, from inappropriate prescribing based on genetic test results to failure to test and informed consent issues. Physicians could face liability for prescribing opioids after a positive test result, or for denying pain medication due to false positive test results, or for prescribing opioids to patients despite negative results that prove false, or for failing to administer the test at all. This article concludes that existing tort frameworks offer insufficient protection for patients and fail to ensure appropriate integration of these tests into clinical practice. And relying on tort law will not mitigate the individual and societal harms raised by the introduction of a polygenic risk score test for opioid use disorder.

The high cost of medication is a common challenge for many Americans. This is particularly true for patients served by Federally Qualified Health Centers (FQHCs) that often have low income and are medically vulnerable. In this article I discuss the history and purpose behind the creation of the 340B drug pricing program, legislation enacted to limit the cost of drugs used by patients covered by Medicaid that requires drug manufacturers to enter into discount agreements with the Health Resources and Services Administration (HRSA), and how for years this program has been used to finance health care provision for communities that use FQHCs as their source of primary care. Additionally, I will discuss the history behind the creation of FQHCs and how that mission is affected by litigation and proposed reforms that would limit the ability of FQHCs to leverage savings and revenue from the program to fund health services for their patient populations. I argue that reinforcement of existing discounting practices, along with limited reforms of the 340B program are necessary to avoid disruption of health care provision for patients of FQHCs and to ensure that the 340B program continues to perform as intended.

Patients experiencing pregnancy complications have died after being denied emergency medical treatment in states with restrictive abortion laws even where the laws ostensibly allow pregnancy termination when necessary to save a patient’s life. For patients experiencing miscarriage, premature rupture of membranes, placental abruption, severe hypertensive disorders, ectopic pregnancy, and other conditions, a treating physician’s decision about whether their condition qualifies as an emergency under state law can mean the difference between life and death. Physicians who treat obstetric emergencies report being torn between delivering evidence-based care and protecting themselves from criminal prosecution. This article frames physician decision-making about emergency abortion within the larger institutional context in which those decisions arise, providing a taxonomy of the myriad hospital policies that impact physicians who treat obstetric emergencies. It evaluates these institutional procedures’ efficacy with respect to the dual goals of protecting pregnant patients’ health and limiting providers’ exposure to legal risk. The article concludes that the optimal institutional approach grants full deference to physicians’ clinical judgment while also providing robust institutional support in the form of comprehensive legal guidance and guaranteed legal representation for physicians who are prosecuted for making good faith medical judgments. It calls upon hospitals to re-commit to their primary mission of providing high quality patient care, and to dedicate resources to supporting physicians who deliver evidence-based care despite unrelenting legal and political pressures.

This article will focus on the use of AI tools in diagnosis and patient treatment in hospitals and health care systems.2 AI vendors promise efficiencies in workplaces: various forms of AI are already being developed to read x-rays and other medical scans, to diagnose a wide range of patient conditions, and to offer a partnership (or risk displacement) of physicians. AI is being pushed to transform medical diagnostics, care quality, patient safety, clinical experience, and efficiencies all over hospital operations. These AI technologies however are still novel and new, and studies proving efficacy or disclaiming it are often based on small scale studies or other limitations.

I will take a quick look at the tools that comprise the use of AI in health care and the claims of effectiveness of AI alone or in partnership with physicians in making clinical decisions. I will then look at the ways in which AI can fail to meet its promises, causing serious harms. Finally, I will examine the prospects of a hybrid regulatory/liability model to regulate AI risks as hospitals and providers expand their uses of AI tools.