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.

New health care devices, including at-home diagnostic devices, are generating and aggregating data on patients’ health at a staggering pace. Yet much of that data is inaccessible because it is held in data siloes, most often cloud services controlled by device manufacturers. This proprietary siloing of patient data is problematic from ethical, economic, scientific, and broad public policy perspectives. This chapter frames these concerns and begins to sketch a regulatory framework for patient access to health care device data. As with other consumer data, breaking down siloes and securing patients’ access to their device data safeguards patients’ ownership interests, promotes patients’ ability to maintain and repair their equipment, and encourages interoperability and competition. Yet, data access is especially important for health data: It allows patients to make informed decisions about their own care, and it enables motivated citizen-scientists to study their own conditions and innovate in response to them. Patient access to device data may also be a first step toward building publicly accessible, responsibly governed datasets of so-called “real-world evidence” – which are increasingly essential to validate the accuracy and reliability of current diagnostic devices – and to invent and validate future devices, drugs, and other precision medicine interventions. These interests motivate the development of our proposed framework. Drawing from related experiences with clinical trial data and electronic health records, this chapter identifies the key considerations for a framework that protects key interests, such as privacy and data security, while unlocking the benefits of broader data sharing.

Medical devices increasingly include software components, which facilitate remote patient monitoring. The introduction of software into previously analog medical devices, as well as innovation in software-driven devices, may introduce new safety concerns – all the more so when such devices are used in patients’ homes, well outside of traditional health care delivery settings. We review four key mechanisms for the post-market surveillance of medical devices in the United States: (1) Post-market trials and registries; (2) manufacturing plant inspections; (3) adverse event reporting; and (4) recalls. We use comprehensive regulatory data documenting adverse events and recalls to describe trends in the post-market safety of medical devices, based on the presence or absence of software. Overall, devices with software are associated with more reported adverse events (i.e. individual injuries and deaths) and more high-severity recalls, compared to devices without software. However, in subgroup analyses of individual medical specialties, we consistently observe differences in recall probability but do not consistently detect differences in adverse events. These results suggest that adverse events are a noisy signal of post-market safety and not necessarily a reliable predictor of subsequent recalls. As patients and health care providers weigh the benefits of new remote monitoring technologies against potential safety issues, they should not assume that safety concerns will be readily identifiable through existing post-market surveillance mechanisms. Both health care providers and developers of remote patient monitoring technologies should therefore consider how they might proactively ensure that newly introduced remote patient monitoring technologies work safely and as intended.

As technological improvements advance, the use of remote monitoring is among the new diagnostic tools that have become a growing part of medical care delivery. But reliance on technologies also challenges the traditional liability schemes that exist to deter negligent physician behavior and compensate injured patients. Liability can arise at each point in a remote monitoring system, from when information is gathered by a device, to when it is processed by an algorithm, and, finally, used by a physician. This chapter explores how different types of liability might arise for device manufacturers and physicians at each of these stages, outlining the main legal rules and complicating factors.

In the Chinese context, “internet plus health care” (IPHC) is introduced as an umbrella term to mean the use of digital technologies to support the delivery of health care and health-related services. IPHC holds great potential to strengthen China’s health system, transform the delivery of health services, and improve equitable, affordable, and universal access to health care. The Chinese government has adopted a variety of regulatory and policy instruments to facilitate the utilization of IPHC before, during, and after COVID-19. This article provides an overview of the development of IPHC in China, with a focus on the regulatory landscape. It then analyzes the challenges that remain in the regulatory framework and concludes with recommendations for furthering the development and utilization of IPHC in the post-COVID-19 era.

The chapter assesses empirical and theoretical descriptions of the transmission mechanism of monetary policy, and discusses why the concept of "R-star" is unlikely to provide a useful practical guidepost for monetary policy.