AI support for ethical decision-making around resuscitation: proceed with care

Abstract

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.

Autonomy-based criticisms of the patient preference predictor

“…Similarly, in a qualitative study, most physicians were open to the use of an artificial intelligence based resuscitation decision algorithm. 8 These data suggest that patients' and doctors' preferences provide support for rather than an objection to the use of a PPP.…”

“…Several algorithms have been proposed in the literature, including the autonomy algorithm, an artificial intelligence based resuscitation algorithm and a patient preference predictor (PPP). [8][9][10] These algorithms vary in a number of respects, including the specific information on which they base their predictions. The autonomy algorithm would train to make decisions based on electronic medical records.…”

“…Consistent with this trend, several commentators have proposed to try to address the challenges with surrogate decision making by supplementing current practice with statistical algorithms which provide a prediction of the patient’s preferred treatment. Several algorithms have been proposed in the literature, including the autonomy algorithm, an artificial intelligence based resuscitation algorithm and a patient preference predictor (PPP) 8–10. These algorithms vary in a number of respects, including the specific information on which they base their predictions.…”

Advance care planning evaluation: a scoping review of best research practice

“…Hopefully these contributions pave the way towards a better understanding of the impact of ACP based on studies that have investigated goal concordant care. Drawing on more rigorous guidelines for assessing goal concordant care, it would also be meaningful to conduct studies that allow for comparison between the differential impact of various forms of advance care planning -including innovative approaches such as AI-based tools [26,27]-in various settings to determine if certain approaches are more effective.…”

Ethics of the algorithmic prediction of goal of care preferences: from theory to practice

“…1 Researchers have proposed to address the aforementioned challenges by designing and implementing algorithms that would compute the most likely preferred treatment of the incapacitated patient. [8][9][10] To the best of our knowledge, the first of these proposals dates back to 2010, when Rid and Wendler introduced the idea of using patients' sociodemographic data to predict preferred treatment options. 1 Since 2010, other algorithmic proposals have been made: they all share the idea that algorithms can learn patterns in data that correlate individual-level information to preferred treatments for a variety of clinical scenarios.…”