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Artificial intelligence (AI) could improve the efficiency and accuracy of health care delivery, but how will AI influence the patient-clinician relationship? While many suggest that AI might improve the patient-clinician relationship, various underlying assumptions will need to be addressed to bring these potential benefits to fruition. Will off-loading tedious work result in less time spent on administrative burden during patient visits? If so, will clinicians use this extra time to engage relationally with their patients? Moreover, given the desire and opportunity, will clinicians have the ability to engage in effective relationship building with their patients? In order for the best-case scenario to become a reality, clinicians and technology developers must recognize and address these assumptions during the development of AI and its implementation in health care.As the field of medicine shifts from a paternalistic to a more patient-centered orientation, the dynamics of shared decision making become increasingly complicated. International globalization and national socioeconomic differences have added unintended difficulties to culturally sensitive communication between physician and patient, which can contribute to the growing erosion of clinician empathy. This article offers a strategy for teaching students how to enter into conversations about shared decision making by bolstering their empathy as a result of exposing them to the many variables outside of their patients' control. learn more Patients' historical and cultural context, gender identity, sexual orientation, and common assumptions about clinicians as well as institutional biases can severely limit students' ability to integrate patients' value-laden preferences into shared decision making about health care.Illness and injury often entail lasting health and social consequences beyond the acute event. During the immediate and long-term recovery period, consequences of illness or injury can often be mitigated and addressed. As patients and their clinicians discuss care decisions, whether for initial or ongoing management of illness or injury, they must consider patients' personal goals of recovery alongside possible clinical outcomes to choose the best path forward. Understanding the recovery process and patients' and clinicians' decision making requires clarifying the concept of recovery and its significance. This article will describe how shared decision making can support the recovery process using a case example of brachial plexus injury.Shared decision making (SDM) is used in adult and pediatric practice for both its ethical and its practical benefits. However, its use is complicated with adolescents whose emerging and relational autonomy is distinct from that of adults, who make decisions independently, and children, whose parents make decisions for them. This hypothetical case scenario and commentary provide clinicians with a practical and stepwise approach to SDM with adolescents as well as guidance when SDM breaks down.Shared decision making honors patient autonomy and improves patient comprehension and therefore should be a part of every clinical decision a patient makes. Use of shared decision making in research informed consent conversations is more complicated due to diverse and potentially divergent investigator and patient interests, along with the presence of clinical equipoise. This article clarifies these different interests and discusses ways in which shared decision making can be applied in research. Provided there is transparency about competing interests, patient-centered and values-focused communication approaches embodied in shared decision making can support the ethical recruitment of patients for clinical research.Shared decision making honors patient autonomy, particularly for preference-sensitive care decisions. Shared decision making can be challenging, however, when patients have impaired decision-making capacity. Here, after presenting an illustrative case example, this paper proposes a capacity-adjusted "sliding scale" approach to shared decision making.Shared decision making is best utilized when a decision is preference sensitive. However, a consequence of choosing between one of several reasonable options is decisional regret wishing a different decision had been made. In this vignette, a patient chooses mastectomy to avoid radiotherapy. However, postoperatively, she regrets the more disfiguring operation and wishes she had picked the other option lumpectomy and radiation. Although the physician might view decisional regret as a failure of shared decision making, the physician should reflect on the process by which the decision was made. If the patient's wishes and values were explored and the decision was made in keeping with those values, decisional regret should be viewed as a consequence of decision making, not necessarily as a failure of shared decision making.The COVID-19 pandemic, which is especially dangerous to older people, has disrupted the lives of older people and their family caregivers. This commentary outlines the adaptive and emerging practices in formal supportive services for family caregivers, the changing types of support that family caregivers are providing to their older relatives, and the ways family caregivers are seeking informal caregiving support during the COVID-19 outbreak.Unhealthy alcohol use is prevalent among persons living with HIV (PLWH). Aging and increased survival of PLWH on antiretroviral therapy (ART) are complicated by metabolic dysregulation and increased risk of insulin resistance (IR) and diabetes mellitus. The objective of this study was to determine the prevalence and association of IR with unhealthy alcohol use in adult in-care PLWH. A cross-sectional analysis of metabolic parameters and alcohol use characteristics was conducted in adult PLWH enrolled in the New Orleans Alcohol Use in HIV (NOAH) Study. IR was estimated using homeostatic model assessment (HOMA-IR), triglyceride index, and McAuley index and beta cell function (HOMA-β). Alcohol use was assessed using Alcohol Use Disorders Identification Test (AUDIT)-C, 30-day timeline followback (TLFB), lifetime drinking history, and phosphatidylethanol (PEth) measures. A total of 351 participants, with a mean age [±standard deviation (SD)] of 48.1 ± 10.4 years, were included (69.6% male). Of these, 57% had an AUDIT-C score of 4 or greater, indicating unhealthy alcohol use.