According to a standard account of patient decision-making capacity, patients can provide ethically valid consent or refusal only if they are able to understand and appreciate their medical c...

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Clinicians are often called upon to assess the capacity of a patient to appoint a healthcare agent. Although a consensus has emerged that the standard for such assessment should differ from that for capacity to render specific healthcare decisions, exactly what standard should be employed remains unsettled and differs by jurisdiction. The current models in use draw heavily upon analogous methods used in clinical assessment, such as the “four skills” approach. This essay proposes an alternative model that relies upon categorization (...) and sliding scale risk assessment that can be used to determine to how much scrutiny the proxy appointment should be subjected and how much certainty of accuracy should be required in order to maximize the patient’s autonomy and ensure that her underlying wishes are met. (shrink)