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)

Background: Current practice frequently fails to provide care consistent with the preferences of decisionally-incapacitated patients. It also imposes significant emotional burden on their surrogates. Algorithmic-based patient preference predictors (PPPs) have been proposed as a possible way to address these two concerns. While previous research found that patients strongly support the use of PPPs, the views of surrogates are unknown. The present study thus assessed the views of experienced surrogates regarding the possible use of PPPs as a means to help make (...) treatment decisions for decisionally-incapacitated patients. -/- Methods: This qualitative study used semi-structured interviews to determine the views of experienced surrogates [n=26] who were identified from two academic medical centers and two community hospitals. The primary outcomes were respondents’ overall level of support for the idea of using PPPs and the themes related to their views on how a PPP should be used, if at all, in practice. -/- Results: Overall, 21 participants supported the idea of using PPPs. The remaining five indicated that they would not use a PPP because they made decisions based on the patient’s best interests, not based on substituted judgment. Major themes which emerged were that surrogates, not the patient’s preferences, should determine how treatment decisions are made, and concern that PPPs might be used to deny expensive care or be biased against minority groups. -/- Conclusions: Surrogates, like patients, strongly support the idea of using PPPs to help make treatment decisions for decisionally-incapacitated patients. These findings provide support for developing a PPP and assessing it in practice. At the same time, patients and surrogates disagree over whose preferences should determine how treatment decisions are made, including whether to use a PPP. These findings reveal a fundamental disagreement regarding the guiding principles for surrogate decision-making. Future research is needed to assess this disagreement and consider ways to address it. (shrink)

Volume 19, Issue 11, November 2019, Page 9-12.

Computers are already approving financial transactions, controlling electrical supplies, and driving trains. Soon, service robots will be taking care of the elderly in their homes, and military robots will have their own targeting and firing protocols. Colin Allen and Wendell Wallach argue that as robots take on more and more responsibility, they must be programmed with moral decision-making abilities, for our own safety. Taking a fast paced tour through the latest thinking about philosophical ethics and artificial intelligence, the authors argue (...) that even if full moral agency for machines is a long way off, it is already necessary to start building a kind of functional morality, in which artificial moral agents have some basic ethical sensitivity. But the standard ethical theories don't seem adequate, and more socially engaged and engaging robots will be needed. As the authors show, the quest to build machines that are capable of telling right from wrong has begun. -/- Moral Machines is the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics. (shrink)

In order to build on the information that was obtained in the course of the first study, a follow-up survey was conducted first by phone and subsequently in a written form between August and October 2014. We contacted 1.858 hospitals in all of Germany for the follow-up survey by phone. In cases where a hospital had not participated in the first study, the willingness to participate in the follow-up survey was established in advance. The survey’s dispatch was ensured in the (...) case of acceptance. The same structured survey was used as a research tool. The data of the first study and the follow-up survey were merged and evaluated. 654 surveys could be evaluated altogether. Our survey by phone revealed that 912 hospitals in all of Germany have at least one form of clinical ethics consultation available. The health care ethics committee is the most frequently implemented structure of clinical ethics consultation. The implementation of clinical ethics consultation is dependent on sponsorship and hospital size. Recommendations to deliver structures of clinical ethics consultations have a smaller influence on the implementation compared to the certification by KTQ respectively proCum Cert. The rate of implementation in regard to the structures of clinical ethics consultation has steadily increased for years. The establishment of clinical ethics consultation in German hospitals should be further promoted. It would be desirable further to develop suitable juridical regulations in order to implement clinical ethics consultation as well as structural parameters. (shrink)

Answering the design questions inherent to building and deploying machine learning tools —based on algorithms that can learn from and make predictions on large data sets without being explicitl...

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical (...) frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

It has recently been proposed to incorporate the use of a “Patient Preference Predictor” (PPP) into the process of making treatment decisions for incapacitated patients. A PPP would predict which treatment option a given incapacitated patient would most likely prefer, based on the individual’s characteristics and information on what treatment preferences are correlated with these characteristics. Including a PPP in the shared decision-making process between clinicians and surrogates has the potential to better realize important ethical goals for making treatment decisions (...) for incapacitated patients. However, developing and implementing a PPP poses significant practical challenges. The present paper discusses these practical challenges and considers ways to address them. (shrink)

There are substantial data establishing that surrogates are often mistaken in predicting what treatments incompetent patients would have wanted and that supplements such as advance directives have not resulted in significant improvements. Rid and Wendler’s Patient Preference Predictor (PPP) proposal will attempt to gather data about what similar patients would prefer in a variety of treatment choices. It accepts the usual goal of patient autonomy and the Substituted Judgment principle for surrogate decisions. I provide reasons for questioning sole reliance on (...) autonomy when surrogates, not patients, are deciding and for giving some independent weight to patient well-being. Moreover, that family members, typical surrogates, will know best what the patient if incompetent would have wanted is not the only reason why they are chosen. The more pressing problem is that the PPP would fail to remove the more serious mistakes that empirical psychology over the last few decades has shown to infect such decision making. (shrink)

How shall we decide for others who cannot decide for themselves? And who—or what, in the case of artificial intelligence — should make the decision? The present issue of the journal tackles several interrelated topics, many of them having to do with surrogate decision making. For example, the feature article by Jardas et al 1 explores the potential use of artificial intelligence to predict incapacitated patients’ likely treatment preferences based on their sociodemographic characteristics, raising questions about the means by which (...) we come to decide for others. And a clinical ethics round table led by Wilkinson and Pillay2 examines the case of a premature baby on life support whose primary surrogate is herself incapacitated. Together, these examples force us to think more deeply about the meaning and significance of taken-for-granted concepts: respect for autonomy, substituted judgement, best interests. We’ll consider the baby first and then turn to AI. “Baby T” is a critically ill newborn delivered prematurely by emergency caesarean section. The mother had entered into a surrogacy arrangement with a same-sex male couple, the intended parents, who were to take over the baby’s care after birth—just as soon as a formal parental order could be obtained through the court. Until then, the birth mother, who had used her own eggs to conceive Baby T along with sperm from an unidentified donor, would have legal and ethical responsibility to decide about the baby’s care. Unfortunately, she too was in critical condition, having fallen unconscious prior to delivery due to a sudden brain haemorrhage. She remained unconscious, and thus incapacitated, during a crucial period in which time-sensitive decisions about Baby T’s care needed to be made, including whether to continue life support. Given the mother’s incapacity, who should …. (shrink)

Background In the last years, there has been an increase in publication of systematic reviews of normative literature or of normative information in bioethics. The aim of a systematic review is to search, select, analyse and synthesise literature in a transparent and systematic way in order to provide a comprehensive and unbiased overview of the information sought, predominantly as a basis for informed decision-making in health care. Traditionally, one part of the procedure when conducting a systematic review is an appraisal (...) of the quality of the literature that could be included. Main text However, while there are established methods and standards for appraising e.g. clinical studies or other empirical research, quality appraisal of normative literature in the context of a systematic review is still rather a conundrum – not only is it unclear how it could or should be done, but also the question whether it necessarily must be done is not settled yet. Based on a pragmatic definition of “normative literature” as well as on a typology of different types of systematic reviews of normative literature/information, this paper identifies and critically discusses three possible strategies of conducting quality appraisal. Conclusions The paper will argue that none of the three strategies is able to provide a general and satisfying solution to the problems associated with quality appraisal of normative literature/information. Still, the discussion of the three strategies allows outlining minimal conditions that elaborated strategies have to meet in future, and facilitates sketching a theoretically and practically promising strategy. (shrink)

Evaluating clinical ethics support services has been hailed as important research task. At the same time, there is considerable debate about how to evaluate CESS appropriately. The criticism, which has been aired, refers to normative as well as empirical aspects of evaluating CESS. In this paper, we argue that a first necessary step for progress is to better understand the intervention in CESS. Tools of complex intervention research methodology may provide relevant means in this respect. In a first step, we (...) introduce principles of “complex intervention research” and show how CESS fulfil the criteria of “complex interventions”. In a second step, we develop a generic “conceptual framework” for “ethics consultation on request” as standard for many forms of ethics consultation in clinical ethics practice. We apply this conceptual framework to the model of “bioethics mediation” to make explicit the specific structural and procedural elements of this form of ethics consultation on request. In a final step we conduct a comparative analysis of two different types of CESS, which have been subject to evaluation research: “proactive ethics consultation” and “moral case deliberation” and discuss implications for evaluating both types of CESS. To make explicit different premises of implemented CESS interventions by means of conceptual frameworks can inform the search for sound empirical evaluation of CESS. In addition, such work provides a starting point for further reflection about what it means to offer “good” CESS. (shrink)

Artificial intelligence is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates (...) both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making. (shrink)

In their proof-of-concept study, Meier et al. built an algorithm to aid ethical decision making. In the limitations section of their paper, the authors state a frequently cited ax...

Background: Ethics support services are growing in Europe to help doctors in dealing with ethical difficulties. Currently, insufficient attention has been focused on the experiences of doctors who have faced ethical difficulties in these countries to provide an evidence base for the development of these services.Methods: A survey instrument was adapted to explore the types of ethical dilemma faced by European doctors, how they ranked the difficulty of these dilemmas, their satisfaction with the resolution of a recent ethically difficult case (...) and the types of help they would consider useful. The questionnaire was translated and given to general internists in Norway, Switzerland, Italy and the UK.Results: Survey respondents ranged in age from 28 to 82 years, and averaged 25 years in practice. Only a minority reported having access to ethics consultation in individual cases. The ethical difficulties most often reported as being encountered were uncertain or impaired decision-making capacity , disagreement among caregivers and limitation of treatment at the end of life . The frequency of most ethical difficulties varied among countries, as did the type of issue considered most difficult. The types of help most often identified as potentially useful were professional reassurance about the decision being correct , someone capable of providing specific advice , help in weighing outcomes and clarification of the issues . Few of the types of help expected to be useful varied among countries.Conclusion: Cultural differences may indeed influence how doctors perceive ethical difficulties. The type of help needed, however, did not vary markedly. The general structure of ethics support services would not have to be radically altered to suit cultural variations among the surveyed countries. (shrink)

Meier et al. describe a pilot study that developed METHAD, an AI-based Medical Ethics Advisor tool that draws on the principlism approach and was tested using text-book cases and clinical et...

The standard approach to treatment decision making for incapacitated patients often fails to provide treatment consistent with the patient’s preferences and values and places significant stress on surrogate decision makers. These shortcomings provide compelling reason to search for methods to improve current practice. Shared decision making between surrogates and clinicians has important advantages, but it does not provide a way to determine patients’ treatment preferences. Hence, shared decision making leaves families with the stressful challenge of identifying the patient’s preferred treatment (...) option. To address this concern, the present paper proposes to incorporate the use of a “Patient Preference Predictor” (PPP) into the shared decision-making process between surrogates and clinicians. A PPP would predict which treatment option a given incapacitated patient would most likely prefer, based on the individual’s characteristics and information on what treatment preferences are correlated with these characteristics. Use of a PPP is likely to increase the chances that incapacitated patients are treated consistent with their preferences and values and might reduce the stress and burden on their surrogates. Including a PPP in the shared decision-making process therefore has the potential to realize important ethical goals for making treatment decisions for incapacitated patients. The present paper justifies this approach on conceptual and normative grounds. (shrink)

In their proof-of-concept, Meier and colleagues describe the purpose and programming decisions underpinning Medical Ethics Advisor, an automated decision support system used t...

Allowing algorithms to guide or determine decision-making in ethically complex situations, and eventually satisfying the need for good clinical ethics consultation work, is a philosophically intere...

Significant criticisms have been raised regarding the ethical and psychological basis of living wills. Various solutions to address these criticisms have been advanced, such as the use of surrogate decision makers alone or data science‐driven algorithms. These proposals share a fundamental weakness: they focus on resolving the problems of living wills, and, in the process, lose sight of the underlying ethical principle of advance care planning, autonomy. By suggesting that the same sweeping solutions, without opportunities for choice, be applied to (...) all, individual patients are treated as population‐level groups—as a theoretical patient who represents a population, not the specific patient crafting his or her individualized future care plans. Instead, advance care planning can be improved through a multimodal approach that both mitigates cognitive biases and allows for customization of the decision‐making process by allowing for the incorporation of a variety of methods of advance care planning. (shrink)

In October 2021, the Allen Institute for Artificial Intelligence publicly released Delphi, an artificial intelligence system trained to make general moral decisions (Allen Institute for Artifi...

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients’ preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%–80% reliability of people’s preferences for future medical decisions—a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of (...) persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed. (shrink)

In this paper, I set out two ethical complications for Rid and Wendler’s proposal that a “Patient Preference Predictor” (PPP) should be used to aid decision making about incapacitated patients’ care. Both of these worries concern how a PPP might categorize patients. In the first section of the paper, I set out some general considerations about the “ethics of apt categorization” within stratified medicine and show how these challenge certain PPPs. In the second section, I argue for a more specific—but (...) more contentious—claim that proper respect for the autonomy of incapacitated patients might require us to act on reasons which they could endorse and show how this claim places important limits on the categories employed by an ethically acceptable PPP. The conclusion shows how these concerns about apt categorization relate to more familiar worries about Rid and Wendler’s proposals. (shrink)

The patient preference predictor is a proposed computer-based algorithm that would predict the treatment preferences of decisionally incapacitated patients. Incorporation of a PPP into the decision-making process has the potential to improve implementation of the substituted judgement standard by providing more accurate predictions of patients’ treatment preferences than reliance on surrogates alone. Yet, critics argue that methods for making treatment decisions for incapacitated patients should be judged on a number of factors beyond simply providing them with the treatments they would (...) have chosen for themselves. These factors include the extent to which the decision-making process recognises patients’ freedom to choose and relies on evidence the patient themselves would take into account when making treatment decisions. These critics conclude that use of a PPP should be rejected on the grounds that it is inconsistent with these factors, especially as they relate to proper respect for patient autonomy. In this paper, we review and evaluate these criticisms. We argue that they do not provide reason to reject use of a PPP, thus supporting efforts to develop a full-scale PPP and to evaluate it in practice. There are no data in this work. (shrink)

Aim: To assess ethical issues in everyday clinical practice among physicians and nurses of the University Hospital Rijeka, Rijeka, Croatia.Subjects and methods: We surveyed the entire population of internal medicine, oncology and intensive care specialists and associated nurses employed at the University Hospital Rijeka, Rijeka, Croatia . An anonymous questionnaire was used to explore the type and frequency of ethical dilemmas, rank of their difficulty, access to and use of ethics support services, training in ethics and confidence about knowledge in (...) ethics. Physicians ranged in age from 27 to 61 years, and nurses , from 20 to 52.Results: The most frequent ethical dilemmas concerned uncertain or impaired decision-making capacity , limitation of treatment at the end of life and disagreements among family members . The most difficult dilemmas concerned euthanasia and physician-assisted suicide and limitation of treatment at the end of life . Only a minority reported ever using any kind of ethics support services or being very confident about knowledge in ethics .Conclusions: Similar ethical difficulties are present in the clinical practice of both physicians and nurses, with important differences in access and use of ethics support services. A need for systematic ethics educational activities was identified. Inclusion of individual ethics consultants in Croatian healthcare ethics support services is strongly advised. (shrink)

The increasing accuracy of algorithms to predict values and preferences raises the possibility that artificial intelligence technology will be able to serve as a surrogate decision-maker for incapacitated patients. Following Camillo Lamanna and Lauren Byrne, we call this technology the autonomy algorithm. Such an algorithm would mine medical research, health records, and social media data to predict patient treatment preferences. The possibility of developing the AA raises the ethical question of whether the AA or a relative ought to serve as (...) surrogate decision-maker in cases where the patient has not issued a medical power of attorney. We argue that in such cases, and against the standard practice of vesting familial surrogates with decision making authority, the AA should have sole decision-making authority. This is because the AA will likely be better at predicting what treatment option the patient would have chosen. It would also be better at avoiding bias and, therefore, choosing in a more patient-centered manner. Furthermore, we argue that these considerations override any moral weight of the patient’s special relationship with their relatives. (shrink)

Machine learning and other forms of artificial intelligence can improve parts of clinical decision making regarding the gathering and analysis of data, the detection of disease, and the provis...

Bioethics, Volume 36, Issue 2, Page 134-142, February 2022.

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to the (...) lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the kind of broad (...) spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)

Background Non-adherence to medication is associated with increased risk of relapse in patients with bipolar disorder. Objectives To validate patient-evaluated adherence to medication measured via smartphones against validated adherence questionnaire; and investigate characteristics for adherence to medication measured via smartphones. Methods Patients with BD evaluated adherence to medication daily for 6–9 months via smartphones. The Medication Adherence Rating Scale and the Rogers’ Empowerment questionnaires were filled out. The 17-item Hamilton Depression Rating Scale, the Young Mania Rating Scale and the Functional (...) Assessment Short Test were clinically rated. Data were collected multiple times per patient. The present study represents exploratory pooled reanalyses of data collected as part of two randomised controlled trials. Findings During the study 90.50% of the days were evaluated as ‘medication taken’, 6.91% as ‘medication taken with changes’ and 2.59% as ‘medication not taken’. Adherence to medication measured via smartphones was valid compared with the MARS. Younger age and longer illness duration were significant predictors for non-adherence to medication. Decreased affective symptoms measured with smartphone-based patient-reported mood and clinical ratings as well as decreased empowerment were associated with non-adherence. Conclusions Smartphone-based monitoring of adherence to medication was valid compared with validated adherence questionnaire. Younger age and longer illness duration were predictors for non-adherence. Increased empowerment was associated with adherence. Clinical implications Using smartphones for empowerment of adherence using patient-reported measures may be helpful in everyday clinical settings. Trial registration number NCT01446406 and NCT02221336. (shrink)

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. (shrink)

When considering the introduction of AI to support medical decision-making, one must take an end-to-end, holistic approach to development, evaluation, integration and governance. (Cabitza and Zeito...

A significant proportion of elderly and psychiatric patients do not have the capacity to make health care decisions. We suggest that machine learning technologies could be harnessed to integrate data mined from electronic health records (EHRs) and social media in order to estimate the confidence of the prediction that a patient would consent to a given treatment. We call this process, which takes data about patients as input and derives a confidence estimate for a particular patient’s predicted health care-related decision (...) as an output, the autonomy algorithm. We suggest that the proposed algorithm would result in more accurate predictions than existing methods, which are resource intensive and consider only small patient cohorts. This algorithm could become a valuable tool in medical decision-making processes, augmenting the capacity of all people to make health care decisions in difficult situations. (shrink)