RESUMEN
Purpose: The use of coercion, in a clinical context as imposing a measure against a patient's opposition or declared will, can occur in various forms in intensive care units (ICU). One prime example of a formal coercive measure in the ICU is the use of restraints, which are applied for patients' own safety. Through a database search, we sought to evaluate patient experiences related to coercive measures. Results: For this scoping review, clinical databases were searched for qualitative studies. A total of nine were identified that fulfilled the inclusion and the CASP criteria. Common themes emerging from the studies on patient experiences included communication issues, delirium, and emotional reactions. Statements from patients revealed feelings of compromised autonomy and dignity that came with a loss of control. Physical restraints were only one concrete manifestation of formal coercion as perceived by patients in the ICU setting. Conclusion: There are few qualitative studies focusing on patient experiences of formal coercive measures in the ICU. In addition to the experience of restricted physical movement, the perception of loss of control, loss of dignity, and loss of autonomy suggests that restraining measures are just one element in a setting that may be perceived as informal coercion.
RESUMEN
Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts.
