Autonomy and capacity: the ethical and legal considerations

This article discusses the important concept of autonomy and what this means for patients and their families. This concept is the first of the four ethical principles identified by Beauchamp and Childress (2019) and is an important legal issue. First, the ethical position of autonomy will be explored, before moving on to discuss the legal aspects. Next, definitions will be presented, and the relevant and pertinent parts of the Nursing and Midwifery Council (NMC) (2018) code introduced. Finally, closely linked to autonomy is capacity. This link will be discussed, highlighting the processes and considerations that need to be considered if a person’s capacity is reduced.

The recording of mental health consultations by patients: clinical, ethical and legal considerations

Summary The topic of patients recording healthcare consultations has been previously debated in the literature, but little consideration has been given to the risks and benefits of such recordings in the context of mental health assessments and treatment. This issue is of growing importance given the increasing use of technology in healthcare and the recent increase in online healthcare services, largely accelerated by the COVID-19 pandemic. We discuss the clinical, ethical and legal considerations relevant to audio or visual recordings of mental health consultations by patients, with reference to existing UK guidance and the inclusion of a patient's perspective.

Decision Deferral in a Human-AI Joint Face-Matching Task: Effects on Human Performance and Trust

This study investigates how human performance and trust are affected by the decision deferral rates of an AI-enabled decision support system in a high criticality domain such as security screening, where ethical and legal considerations prevent full automation. In such domains, deferring cases to a human agent becomes an essential process component. However, the systemic consequences of the rate of deferrals on human performance are unknown. In this study, a face-matching task with an automated face verification system was designed to investigate the effects of varying deferral rates. Results show that higher deferral rates are associated with higher sensitivity and higher workload, but lower throughput and lower trust in the AI. We conclude that deferral rates can affect performance and trust perceptions. The tradeoffs between deferral rate, sensitivity, throughput, and trust need to be considered in designing effective human-AI work systems.

Technology-Based Psychotherapies for Obsessive-Compulsive Disorder

Access to exposure and response prevention (ERP) for the treatment of obsessive-compulsive disorder (OCD) continues to be challenging for many individuals around the world, particularly those living in rural and underserved areas. Innovative technological solutions including mobile applications, internet-based programs, and video telehealth have the potential to expand access to mental health care for OCD. This chapter covers the use of technology in delivering ERP for OCD. First, an overview is provided of the various technology-based solutions that exist and the supporting evidence for each. Then, as video telehealth delivery is the most commonly used and has the most empirical support, the rest of the chapter discusses the benefits and challenges of video telehealth delivery of ERP; covers clinical, ethical, and legal considerations in the delivery of ERP via video telehealth; and provides two case studies illustrating relevant issues.

Unresolved Privacy and Ethics Issues Related to Learning Analytics in Higher Education and Academic Librarianship

Learning analytics involve big data collection, analysis processes, and technology that are used in higher education institutes and academic libraries to support student success and perform organizational assessment. Since these processes require the input of personally identifiable student and patron information to be effective, there are major ethical and legal considerations that must be addressed concerning privacy. This article demonstrates that privacy concerns about learning analytics can be mitigated by requiring informed consent from participants, establishing protocols for the collection and management of personally identifiable information, and advocating privacy rights of patrons. By synthesizing and expanding on viewpoints from the literature, this article offers recommendations pertaining to the collection, analysis, and management of patron data that are gathered for the purpose of learning analytics.