Autonomy-based criticisms of the patient preference predictor

Balancing choice and socioeconomic realities: analyzing behavioral and economic factors in social oocyte cryopreservation decisions

Purpose

This research investigates the influence of personal income, the likelihood of pregnancy from cryopreserved oocytes, and the risk of infertility, on the decision-making process of women. The study employs the economic stated preference framework alongside the Theory of Planned Behavior in order to comprehend the process of decision-making.

Design/methodology/approach

The data had been collected from women between the ages of 18 and 65 via questionnaire employing conjoint analysis (CA). Through the utilization of this methodology, the factors influencing women's choices concerning oocyte cryopreservation were quantified.

Findings

The study identified crucial factors that impact the determination to cryopreserve oocytes, such as personal financial resources, the likelihood of achieving a successful pregnancy using frozen oocytes, and the potential for infertility. The analysis reveals that a considerable number of participants perceive cryopreservation as a feasible alternative for augmenting their prospects for future procreation.

Research implications

The results validate the patterns and the ways in which personal and socioeconomic elements impact choices regarding fertility. This has the potential to inform forthcoming health policies and educational initiatives that aim to provide more comprehensive support for women's fertility decisions.

Social implications

The research highlights the necessity of policy and societal support for women who are contemplating oocyte cryopreservation. It is recommended that public health policies incorporate provisions for state financing of cryopreservation in order to safeguard reproductive autonomy and alleviate the fertility risk linked to the aging process.

Originality/value

His research is unique in that it employs the Theory of Planned Behavior and an economic stated-preference framework to analyze the dynamics of oocyte cryopreservation decisions. This work enhances the existing body of literature by drawing attention to the socio-economic persona factors that influence choices regarding fertility preservation.

Non-Psychiatric Treatment Refusal in Patients with Depression: How Should Surrogate Decision-Makers Represent the Patient's Authentic Wishes?

Patients with mental illness, and depression in particular, present clinicians and surrogate decision-makers with complex ethical dilemmas when they refuse life-sustaining non-psychiatric treatment. When treatment rejection is at variance with the beliefs and preferences that could be expected based on their premorbid or "authentic" self, their capacity to make these decisions may be called into question. If capacity cannot be demonstrated, medical decisions fall to surrogates who are usually advised to decide based on a substituted judgment standard or, when that is not possible, best interest. We propose that in cases where the patient meets the widely accepted cognitive criteria for capacity but is making decisions that appear inauthentic, the surrogate may best uphold patient autonomy by following a "restorative representation" model. We see restorative representation as a subset of substituted judgement in which the decision-maker retains responsibility for deciding as the patient would have, but discerns the decision their "truest self" would make, rather than inferring their current wishes, which are directly influenced by illness. Here we present a case in which the patient's treatment refusal and previously undiagnosed depression led to difficulty determining the patient's authentic wishes and placed a distressing burden on the surrogate decision-maker. We use this case to examine how clinicians and ethicists might better advise surrogates who find themselves making these clinically and emotionally challenging decisions.

Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor

Background

The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.

Methods

We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP.

Results

Sixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries (n = 33) and spinal surgeries (n = 12) were the most common medical event. Studies used demographic (n = 30), pre-event PROMs (n = 52), comorbidities (n = 29), social determinants of health (n = 30), and intraoperative variables (n = 124) as predictors. Thirty-four different PROMs were used as the target outcome. Evaluation metrics varied by task, but performance was overall poor to moderate for the best reported scores. In models that used feature importance, pre-event PROMs were the most predictive of post-event PROMs. Fairness assessments were rare (n = 6). These findings reinforce the necessity of the integrating patient values and preferences, beyond demographic factors, to improve the development of personalized PPP models for incapacitated patients.

Conclusion

The primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for capacitated patients introduces challenges and opportunities for building a personalized PPP for incapacitated patients without advanced directives.

Algorithms advise, humans decide: the evidential role of the patient preference predictor

An AI-based 'patient preference predictor' (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP-that between algorithmic prediction and decision-making-and argue that much of the recent philosophical disagreement stems from this oversight. I show how three prominent objections to the PPP only challenge its use as the sole determinant of a choice, and actually support its use as a source of evidence about patient preferences to inform human decision-making. The upshot is that we should adopt the evidential conception of the PPP and shift our evaluation of this technology towards the ethics of algorithmic prediction, rather than decision-making.

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

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.

Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons

BACKGROUND

Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.

METHODS

PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.

RESULTS

Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.

CONCLUSIONS

The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human-machine interaction, have been neglected in the debate on AI for clinical ethics so far.

TRIAL REGISTRATION

This review is registered at Open Science Framework ( https://osf.io/wvcs9 ).

Response to commentaries: 'autonomy-based criticisms of the patient preference predictor'

The authors respond to four JME commentaries on their Feature Article, 'Autonomy-based criticisms of the patient preference predictor'.

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

Recently, Jardas et al have convincingly defended the patient preference predictor (PPP) against a range of autonomy-based objections. In this response, I propose a new autonomy-based objection to the PPP that is not explicitly discussed by Jardas et al I call it the 'objection from higher-order preferences'. Even if this objection is not sufficient reason to reject the PPP, the objection constitutes a pro tanto reason that is at least as powerful as the ones discussed by Jardas et al.

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

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.