The Patient preference predictor and the objection from higher-order preferences

Mainz, Jakob Thrane

doi:10.1136/jme-2022-108427

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Surrogates can, for example, act as a critical corrective to ensure that the patient is not seen as determined by his environment and deprived of his autonomy 2. Additionally, many patients care about how their treatment decisions are made, who is involved, and who is not 3. As another reason, we would like to point to the limited nature of the data used for AI preference prediction.…”

Section: The Role Of Next Of Kin In Patient Preference Findingmentioning

confidence: 99%

For the sake of multifacetedness. Why artificial intelligence patient preference prediction systems shouldn’t be for next of kin

Tretter

Samhammer

2023

J Med Ethics

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Section: The Role Of Next Of Kin In Patient Preference Findingmentioning

confidence: 99%

For the sake of multifacetedness. Why artificial intelligence patient preference prediction systems shouldn’t be for next of kin

Tretter

Samhammer

2023

J Med Ethics

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“…An additional autonomy-based objection has been raised by Mainz ( 2023 ). Although originally formulated in relation to the PPP, it might also apply to a P4.…”

Section: Autonomy-based Objections To Patient Preference Predictionmentioning

confidence: 99%

“…On a strong formulation of this view, medical decisions made on behalf of formerly autonomous persons should incorporate only the reasons, values, and evidence that the person themselves took, or would have taken, into account when making treatment decisions (John 2014 ; John 2018 ). 3 Other concerns include that patients may have preferences about how treatment decisions are made for them, including how their preferences are identified, not just which treatments they receive (Mainz 2023 ). In other words, it may be that substituted decision-making should consider not only patients’ treatment preferences, but also their process preferences (i.e., the process by which treatment decisions are made or by which their treatment preferences are inferred).…”

Section: Introductionmentioning

confidence: 99%

A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable

Earp,

Porsdam Mann,

Allen

et al. 2024

The American Journal of Bioethics

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When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies.

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“…First, the idea of using algorithms to predict patient’s preferred treatments is fraught with theoretical challenges that pertain to epistemology and ethics. Some of these challenges relate to patient autonomy,14–16 the difficulty of avoiding bias and the importance of addressing explainability given the ‘black box’ nature of many artificial intelligence (AI) algorithms 8. Second, the design, development and use of these algorithms need to address pragmatic and human–computer interaction (HCI) driven challenges, such as safety, reliability and adequate testing to foster usability and trust among clinicians 17 18.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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The Patient preference predictor and the objection from higher-order preferences

Cited by 6 publications

References 10 publications

For the sake of multifacetedness. Why artificial intelligence patient preference prediction systems shouldn’t be for next of kin

For the sake of multifacetedness. Why artificial intelligence patient preference prediction systems shouldn’t be for next of kin

A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable

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

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