Artificial intelligence (AI) is changing our daily life and the way we receive health care. For example, Google hopes to soon start a pilot study for its “AI-powered dermatology tool,” an app with knowledge of 288 skin conditions. The FDA has also already permitted the marketing of similar medical devices, such as Apple’s electrocardiogram (ECG) app. Interestingly, both Google and Apple advertise their direct-to-patient/consumer (DTP/DTC) apps as information tools only that are not intended to provide a diagnosis. This is due to their “over-the-counter” nature, although Apple’s clinical study of the ECG app, for example, correctly diagnosed atrial fibrillation with 98.3 percent sensitivity and 99.6 percent specificity. But do patients and consumers really understand that such and similar medical apps do not replace traditional diagnosis and treatment methods? Moreover, many DTP/DTC medical AI apps for “self-diagnosis” are opaque (“black boxes”), can continuously learn, and are vulnerable to biases. Patients and consumers need to understand the indications for use, the model characteristics, and the risks and limitations of such tools. However, the FDA has not yet developed any labeling standards specifically for AI-based medical devices, let alone for those directly addressed to patients/consumers. This chapter explores not only the benefits of labeling, such as helping patients and consumers to make more informed decisions, but also the potential limitations. It also makes suggestions on the content of labeling for DTP/DTC AI diagnosis apps. In particular, this chapter argues that the advertisement of this technology as “information tools only” rather than “diagnosis tools” is misleading for consumers and patients.

The COVID-19 pandemic revolutionized abortion care. What seemed impossible a few years ago – entirely virtual abortion – is now a reality. The Food and Drug Administration (FDA) has historically required patients to collect abortion medication, a two-drug regimen that terminates a pregnancy in the first ten weeks, in-person at a health care facility. In July 2020, a federal district court suspended that requirement during the pandemic, allowing providers to mail abortion medication directly to patients. In December 2021, President Biden removed the in-person requirement permanently. Over the last two years, virtual clinics have begun offering “no-touch” abortions, eliminating many of the costs associated with travel to an abortion clinic. The FDA’s most recent decision has cleared the way for the supervised mail delivery and pharmacy dispensation of abortion medication. The expansion of virtual clinics, however, faces significant obstacles and limitations – most acutely, the fact that nineteen states prohibit telabortion explicitly or indirectly. This chapter maps the emergence of virtual abortion care and analyzes its significance for early abortion access, particularly in the post-Roe world. It then considers the limits of telabortion, concluding that, over the long term, the portability of abortion medication will test how closely state officials (or anyone else) can police access to early abortion care, even if abortion is banned in a particular state.

Digital tools to diagnose and treat patients evoke four conceptualizations of privacy. An interest in spatial privacy feels violated as the home becomes a site of digital observation and surveillance, inviting a scheme of protections geared to the traditional privacy of the home. Yet many information theorists reject location-based privacy schemes and favor control-over-information theory, stressing people’s rights to notice and consent before disclosing or using their identifiable information. US medical privacy law, including the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, rejects control-over- information theory and is more aptly portrayed as a Nissenbaumian contextual privacy scheme directed at one specific context: Clinical health care (as opposed to health-related transactions outside the clinical context). A fourth alternative would be a uniform scheme of content-based privacy regulations that stratifies the level of privacy protection based on inherent data characteristics (e.g. data about health) without regard to where in the overall economy the data are generated and stored. The European Union (EU)’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act aspire (with partial success) to extend uniform protections. This chapter examines these four visions of privacy and considers their advantages and possible drawbacks when applied to home health care. After comparing the four alternatives, the chapter recommends norms of privacy and data access for digital tools used in home care settings.

Advances in biomarker science are transforming the treatment of Alzheimer’s disease. First, clinicians can now identify Alzheimer’s disease neuropathology in situ, years, or even decades, before the onset of cognitive and functional impairment using biomarkers, particularly measures of amyloid, tau, and neurodegeneration. Second, there is growing evidence that other digital markers – for instance, changes in banking and driving patterns – can be used to identify older adults at increased risk of cognitive decline. As a result of these two advances, there is growing interest in monitoring older adults for incipient changes in cognition that might affect their wellbeing. This monitoring can be no-tech, such as when an older adult asks a friend or family member to let them know if they start repeating questions or retelling stories. Or it can be high-tech, such as installing a device in one’s car that tracks sudden accelerations and decelerations and the distance traveled from home. While there is great enthusiasm for this kind of monitoring among clinicians and researchers, and even, as our own research shows, among some older adults and their family members, other older adults deem monitoring to be an unwanted intrusion on their privacy. This chapter discusses the changing understanding of Alzheimer’s disease risk; presents data on the desire for monitoring, highlighting the potential for intrafamilial conflict; considers the ethical obligations of non-health businesses to collect or disclose information about Alzheimer’s disease risk; and, finally, identifies potential ethical and legal challenges – such as privacy, stigma, and discrimination – with possible solutions.

During the COVID-19 pandemic, precautionary measures were implemented to reduce the spread of SARS-CoV-2, including the introduction of the Acute Hospital Care at Home waiver by the Centers for Medicare & Medicaid Services (CMS). The integration of home hospital services in the US health care delivery system has created new opportunities to address social determinants of health (SDOH) and improve the value of care, such as delivering preventative services at the optimal time, coordinating care across sites, and prioritizing patient needs and preferences. While at-home care programs are not new, emerging technologies have the potential to remove barriers to their adoption – if policymakers get the conditions right. Furthermore, while public and private payers are developing new payment models to address SDOH, little is known regarding the feasibility of their application to home hospital programs across the US. Informed by Mayo Clinic patient and staff interviews in Arizona, Florida, Minnesota, and Wisconsin, this chapter proposes evidence-based policy recommendations to facilitate high-value home hospital care, of which the equitable use of digital tools is a critical component. Regarding statutory reform, it advances a model policy that is flexible enough to incorporate high-value home hospital care not yet conceptualized. Considering reimbursement strategy, this chapter proposes guidelines for payment reform initiatives addressing SDOH to include provisions for access to digital tools that facilitate home hospital care. Lastly, this chapter outlines principles for nurturing a cybersecurity-conscious culture in home hospital programs as digital health care evolves.

Health care delivery is shifting away from the clinic and into the home. Even prior to the COVID-19 pandemic, the use of telehealth, wearable sensors, ambient surveillance, and other products was on the rise. In the coming years, patients will increasingly interact with digital products at every stage of their care, such as using wearable sensors to monitor changes in temperature or blood pressure, conducting self-directed testing before virtually meeting with a physician for a diagnosis, and using smart pills to document their adherence to prescribed treatments. This volume reflects on the explosion of at-home digital health care and explores the ethical, legal, regulatory, and reimbursement impacts of this shift away from the twentieth-century focus on clinics and hospitals toward a more modern health care model. This title is also available as Open Access on Cambridge Core.

This contribution analyzes the impact of digital health care on the European Union (EU) legislative framework concerning cross-border health care and the related reimbursement rules. Traditionally, cross-border health care concerns a situation where a patient crosses an internal EU border. However, with the introduction of digital health care, a patient is no longer required to move to receive cross-border health care. Under EU law, patients have two independent rights to reimbursement for medical expenses in a cross-border situation, embedded in Regulation 833/2004 on Social Security Systems and the Patients’ Rights Directive 2011/24. A striking difference between the Regulation and the Directive lies in the fact that the Directive explicitly addresses digital health care, referring to “telemedicine,” whereas the Regulation does not. Also, whereas under the Directive, cross-border health care is reimbursed at the home-state rate, under the Regulation, cross-border health care is reimbursed at the host-state rate, leaving room for forum shopping for advantageous reimbursement rates. However, as the Regulation refers to “travel” and “temporary residence” in the host state, such forum shopping would not be available for digital cross-border health care. This contribution focusses on these discrepancies between the Directive and the Regulation, and specifically on the consequences for the reimbursement of patients benefitting from cross-border digital health care. Digital and physical cross-border health care reimbursement opportunities are compared, and based on the outcomes, an assessment is made as to whether the Regulation should be updated to be suitable for digital health care.

Remote care technologies help patients connect with their caregivers through monitoring, alerts, anomaly detection, and so on. Due to their nature, remote care technologies cross a number of legal fields, such as privacy and data protection, cybersecurity, and medical devices regulation. This paper aims to close the gap between high-level legal principles and practical implementation by mapping the challenges in European Union (EU) law and combining them with initial results from the TeNDER project. Specifically, we focus on technologies, which create an alert system by combining data sources from electronic health records and connected devices. Using these solutions as a starting point, we analyze the obligations the EU law lays upon the stakeholders, that is, the designers or developers, and the users, who are patients and caregivers. We answer the following research question: Which challenges does EU law pose for designers and users of remote care solutions, and in what manner can those questions be addressed in practice? We then analyze and apply the principles of privacy and data protection (proportionality, lawfulness, and data quality), and cybersecurity notification duties, and discuss the possible classification as a medical device. For all three areas, we use the two-pronged approach from the project, that is, a big-picture description of the legal challenges posed by remote care technologies, and a detailed description of the legal obligations applicable to the developers as well as users (i.e., caregivers and patients). We will follow up our work in repeated impact assessments in order to determine the benefits and pitfalls of the current approach.

In the wake of the 2019 COVID-19 pandemic, at-home digital care has expanded. This change has set off a cascade of effects, including new pathways for information flows that include a wider array of direct-to-consumer companies and products. This new landscape requires a reexamination of the implicit and explicit social contract between patients, clinicians, and the health delivery system: Who has access to personal health information? What is the appropriate role of commercial companies? Are people who continue to receive most of their care in clinical settings subject to the new norms of at-home care? This technology and circumstance drive change in health is not a new phenomenon. In this chapter, we examine the emergence of the Michigan BioTrust for Health in 2009 as an instance of renegotiating the social contract between stakeholders in response to cultural and technological changes that made information more available and accessible to a broader community of users. In the case of the BioTrust, Michigan’s four million “legacy” newborn screening bloodspots and the related health information were repurposed to be more widely available, not only for public health, but also for research. Based on prior research on the ethical and policy implications for patients that were part of the legacy system (i.e. those being asked to make the change from old to new systems of care), we review key findings on attitudes about informed consent and notification, partnerships with commercial companies, and trust. We review consumer preferences and expectations in the context of the BioTrust for Health and the expanded use of newborn screening bloodspots and consider their implications for the governance of at-home digital health care.

The ability to clinically diagnose and treat medical conditions within the home is rapidly becoming a reality for millions of Americans. In parallel, the vast majority of older adults currently report a preference to age in place, in part because of the independence and autonomy this affords, as well as the enhanced ability to socially distance oneself during a pandemic. The interest in receiving long-term services and support in the home is exemplified by the 820,000 Medicaid-eligible Americans on waiting lists nationwide for home- and community-based services (HCBS), with an average wait time of over three years. As a result, many Americans today face the difficult decision of whether to move to a nursing home or stay in their home, facing the risk of falls, medication adherence errors, and other safety challenges. Diagnosing and monitoring health in the home has the potential to abate this distressingly difficult decision as a cost-efficient, patient-centered alternative to reduce HCBS waiting lists and expand the long-term care options for millions of non-Medicaid-eligible Americans. This chapter delineates the ethical, social, legal, and regulatory issues around implementing diagnostic and digital health in the homes of older adults, in the context of this population’s unique vulnerability to abuse, social isolation, declining cognitive health, frailty, and diagnostic error. Broad-based conceptual and practical reforms to modernize HCBS follow, addressing the adoption of self-administered diagnostic tests for highly prevalent chronic conditions, validated decision-support tools to foster consent, and real-time health data acquisition and management strategies that support government oversight and equitable access to home telehealth.

The UK National Health Service (NHS) has committed £250 million toward the deployment of artificial intelligence (AI). One compelling use case involves patient-recorded cardiac waveforms, interpreted in real-time by AI to predict the presence of common, clinically actionable cardiovascular diseases. Waveforms are recorded by a handheld device applied by the patient at home in a self-administered “smart” stethoscope examination. The deployment of such a novel home-based screening program, combining hardware, AI, and a cloud-based administrative platform, raises ethical challenges, including considerations of equity, agency, data rights, and, ultimately, responsibility for safe, effective, and trustworthy implementation. The meaningful use of these devices without direct clinician involvement transfers the responsibility for conducting a diagnostic test with potentially life-threatening consequences onto the patient. The use of patients’ own smartphones and internet connections should also meet the data security standards expected of NHS activity. Additional complexity arises from rapidly evolving questions around data “ownership,” according to European law a term applicable only to the patient from whom the data originate, when “controllership” of patient data falls to commercial entities. Clarifying the appropriate consent mechanism requires the reconciliation of commercial, patient, and health system rights and obligations. Oriented to this real-world clinical setting, this chapter evaluates the ethical considerations of extending home-based, self-administered AI diagnostics in the NHS. It discusses the complex field of stakeholders, including patients, academia, and industry, all ultimately beholden to governmental entities. It proposes a multi-agency approach to balance permissive regulation and deployment (to align with the speed of innovation) against ethical and statutory obligations to safeguard public health. It further argues that a strong centralized approach to carefully evaluating and integrating home-based AI diagnostics is necessary to balance the considerations outlined above. The chapter concludes with specific, transferable policy recommendations applicable to NHS stewardship of this novel diagnostic pathway.