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The ongoing debate within neuroethics concerning the degree to which neuromodulation such as deep brain stimulation (DBS) changes the personality, identity, and agency (PIA) of patients has paid relatively little attention to the perspectives of prospective patients. Even less attention has been given to pediatric populations. To understand patients' views about identity changes due to DBS in obsessive-compulsive disorder (OCD), the authors conducted and analyzed semistructured interviews with adolescent patients with OCD and their parents/caregivers. Patients were asked about projected impacts to PIA generally due to DBS. All patient respondents and half of caregivers reported that DBS would impact patient self-identity in significant ways. For example, many patients expressed how DBS could positively impact identity by allowing them to explore their identities free from OCD. Others voiced concerns that DBS-related resolution of OCD might negatively impact patient agency and authenticity. Half of patients expressed that DBS may positively facilitate social access through relieving symptoms, while half indicated that DBS could increase social stigma. These views give insights into how to approach decision-making and informed consent if DBS for OCD becomes available for adolescents. They also offer insights into adolescent experiences of disability identity and "normalcy" in the context of OCD.
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Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool's computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability in trust criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarily epistemic in nature, focused on accuracy and validity of AI/ML estimates. Trust evaluations considered the nature, integrity and relevance of training data rather than the computational nature of algorithms themselves, suggesting a need to distinguish 'source' from 'functional' explainability. To a lesser extent, trust criteria were also relational (endorsement from others) and sometimes based on personal beliefs and experience. We discuss implications for promoting appropriate and responsible trust calibration for clinical decision-making use AI/ML.
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INTRODUCTION: Pediatric deep brain stimulation (pDBS) is commonly used to manage treatment-resistant primary dystonias with favorable results and more frequently used for secondary dystonia to improve quality of life. There has been little systematic empirical neuroethics research to identify ethical challenges and potential solutions to ensure responsible use of DBS in pediatric populations. METHODS: Clinicians (n = 29) who care for minors with treatment-resistant dystonia were interviewed for their perspectives on the most pressing ethical issues in pDBS. RESULTS: Using thematic content analysis to explore salient themes, clinicians identified four pressing concerns: (1) uncertainty about risks and benefits of pDBS (22/29; 72%) that poses a challenge to informed decision-making; (2) ethically navigating decision-making roles (15/29; 52%), including how best to integrate perspectives from diverse stakeholders (patient, caregiver, clinician) and how to manage surrogate decisions on behalf of pediatric patients with limited capacity to make autonomous decisions; (3) information scarcity effects on informed consent and decision quality (15/29; 52%) in the context of patient and caregivers' expectations for treatment; and (4) narrow regulatory status and access (7/29; 24%) such as the lack of FDA-approved indications that contribute to decision-making uncertainty and liability and potentially limit access to DBS among patients who may benefit from it. CONCLUSION: These results suggest that clinicians are primarily concerned about ethical limitations of making difficult decisions in the absence of informational, regulatory, and financial supports. We discuss two solutions already underway, including supported decision-making to address uncertainty and further data sharing to enhance clinical knowledge and discovery.
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Estimulação Encefálica Profunda , Distonia , Distúrbios Distônicos , Humanos , Criança , Qualidade de Vida , Distúrbios Distônicos/terapia , Consentimento Livre e EsclarecidoRESUMO
In this paper, we contend with whether we still need traditional ethics education as part of healthcare professional training given the abilities of chatGPT (generative pre-trained transformer) and other large language models (LLM). We reflect on common programmatic goals to assess the current strengths and limitations of LLMs in helping to build ethics competencies among future clinicians. Through an actual case analysis, we highlight areas in which chatGPT and other LLMs are conducive to common bioethics education goals. We also comment on where such technologies remain an imperfect substitute for human-led ethics teaching and learning. Finally, we conclude that the relative strengths of chatGPT warrant its consideration as a teaching and learning tool in ethics education in ways that account for current limitations and build in flexibility as the technology evolves.
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Bioethicists today are taking a greater role in the design and implementation of emerging technologies by "embedding" within the development teams and providing their direct guidance and recommendations. Ideally, these collaborations allow ethical considerations to be addressed in an active, iterative, and ongoing process through regular exchanges between ethicists and members of the technological development team. This article discusses a challenge to this embedded ethics approach-namely, that bioethical guidance, even if embraced by the development team in theory, is not easily actionable in situ. Many of the ethical problems at issue in emerging technologies are associated with preexisting structural, socioeconomic, and political factors, making compliance with ethical recommendations sometimes less a matter of choice and more a matter of feasibility. Moreover, incentive structures within these systemic factors maintain them against reform efforts. The authors recommend that embedded bioethicists utilize principles from behavioral science (such as behavioral economics) to better understand and account for these incentive structures so as to encourage the ethically responsible uptake of technological innovations.
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Ciências do Comportamento , Bioética , Humanos , Eticistas , Princípios MoraisAssuntos
Eticistas , Aprendizado de Máquina , Atenção à Saúde , Ética em Pesquisa , Instalações de Saúde , HumanosRESUMO
Background: Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods: We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results: Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion: Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.
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BACKGROUND: Personalized risk (PR) estimates may enhance clinical decision making and risk communication by providing individualized estimates of patient outcomes. We explored stakeholder attitudes toward the utility, acceptability, usefulness and best-practices for integrating PR estimates into patient education and decision making about Left Ventricular Assist Device (LVAD). METHODS AND RESULTS: As part of a 5-year multi-institutional AHRQ project, we conducted 40 interviews with stakeholders (physicians, nurse coordinators, patients, and caregivers), analyzed using Thematic Content Analysis. All stakeholder groups voiced positive views towards integrating PR in decision making. Patients, caregivers and coordinators emphasized that PR can help to better understand a patient's condition and risks, prepare mentally and logistically for likely outcomes, and meaningfully engage in decision making. Physicians felt it can improve their decision making by enhancing insight into outcomes, enhance tailored pre-emptive care, increase confidence in decisions, and reduce bias and subjectivity. All stakeholder groups also raised concerns about accuracy, representativeness and relevance of algorithms; predictive uncertainty; utility in relation to physician's expertise; potential negative reactions among patients; and overreliance. CONCLUSION: Stakeholders are optimistic about integrating PR into clinical decision making, but acceptability depends on prospectively demonstrating accuracy, relevance and evidence that benefits of PR outweigh potential negative impacts on decision making quality.
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Coração Auxiliar , Médicos , Humanos , Tomada de Decisões , Educação de Pacientes como Assunto , AtitudeRESUMO
Metaverse-enabled healthcare is no longer hypothetical. Developers must now contend with ethical, legal and social hazards if they are to overcome the systematic inefficiencies and inequities that exist for patients who seek care in the real world.
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Technological advancements of prostheses in recent years, such as haptic feedback, active power, and machine learning for prosthetic control, have opened new doors for improved functioning, satisfaction, and overall quality of life. However, little attention has been paid to ethical considerations surrounding the development and translation of prosthetic technologies into clinical practice. This article, based on current literature, presents perspectives surrounding ethical considerations from the authors' multidisciplinary views as prosthetists (HG, AM, CLM, MGF), as well as combined research experience working directly with people using prostheses (AM, CLM, MGF), wearable technologies for rehabilitation (MGF, BN), machine learning and artificial intelligence (BN, KKQ), and ethics of advanced technologies (KKQ). The target audience for this article includes developers, manufacturers, and researchers of prosthetic devices and related technology. We present several ethical considerations for current advances in prosthetic technology, as well as topics for future research, that may inform product and policy decisions and positively influence the lives of those who can benefit from advances in prosthetic technology.
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Objective: To explore and compare the perspectives of clinicians and patients on polygenic embryo screening. Design: Qualitative. Subjects: Fifty-three participants: 27 reproductive endocrinology and infertility specialists and 26 patients currently undergoing in vitro fertilization or had done so within the last five years. Main Outcome Measures: Qualitative thematic analysis of interview transcripts. Results: Both clinicians and patients often held favorable views of screening embryos for physical or psychiatric conditions, though clinicians tended to temper their positive attitudes with specific caveats. Clinicians also expressed negative views about screening embryos for traits more often than patients, who generally held more positive views. Most clinicians were either unwilling to discuss or offer polygenic embryo screening to patients or were willing to do so only under certain circumstances, while many patients expressed interest in polygenic embryo screening. Both sets of stakeholders envisioned multiple potential benefits or uses of polygenic embryo screening; the most common included selection and/or prioritization of embryos, receipt of more information about embryos, and preparation for the birth of a predisposed or "affected" child. Both sets of stakeholders also raised multiple potential, interrelated concerns about polygenic embryo screening. The most common concerns among both sets of stakeholders included the potential for different types of "biases" - most often in relation to selection of embryos with preferred genetic chances of traits -, the probabilistic nature of polygenic embryo screening that can complicate patient counseling and/or lead to excessive cycles of in vitro fertilization, and a lack of data from long-term prospective studies supporting the clinical use of polygenic embryo screening. Conclusion: Despite patients' interest in polygenic embryo screening, clinicians feel such screening is premature for clinical application. Though now embryos can be screened for their genetic chances of developing polygenic conditions and traits, many clinicians and patients maintain different attitudes depending on what is specifically screened, despite the blurry distinction between conditions and traits. Considerations raised by these stakeholders may help guide professional societies as they consider developing guidelines to navigate the uncertain terrain of polygenic embryo screening, which is already commercially available.
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Introduction: Deep brain stimulation (DBS) is utilized to treat pediatric refractory dystonia and its use in pediatric patients is expected to grow. One important question concerns the impact of hope and unrealistic optimism on decision-making, especially in "last resort" intervention scenarios such as DBS for refractory conditions. Objective: This study examined stakeholder experiences and perspectives on hope and unrealistic optimism in the context of decision-making about DBS for childhood dystonia and provides insights for clinicians seeking to implement effective communication strategies. Materials and Methods: Semi-structured interviews with clinicians (n = 29) and caregivers (n = 44) were conducted, transcribed, and coded. Results: Using thematic content analysis, four major themes from clinician interviews and five major themes from caregiver interviews related to hopes and expectations were identified. Clinicians expressed concerns about caregiver false hopes (86%, 25/29) and desperation (68.9%, 20/29) in light of DBS being a last resort. As a result, 68.9% of clinicians (20/29) expressed that they intentionally tried to lower caregiver expectations about DBS outcomes. Clinicians also expressed concern that, on the flip side, unrealistic pessimism drives away some patients who might otherwise benefit from DBS (34.5%, 10/29). Caregivers viewed DBS as the last option that they had to try (61.3%, 27/44), and 73% of caregivers (32/44) viewed themselves as having high hopes but reasonable expectations. Fewer than half (43%, 19/44) expressed that they struggled setting outcome expectations due to the uncertainty of DBS, and 50% of post-DBS caregivers (14/28) expressed some negative feelings post treatment due to unmet expectations. 43% of caregivers (19/44) had experiences with clinicians who tried to set low expectations about the potential benefits of DBS. Conclusion: Thoughtful clinician-stakeholder discussion is needed to ensure realistic outcome expectations.
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Approximately 10-20% of children with obsessive-compulsive disorder (OCD) have treatment-resistant presentations, and there is likely interest in developing interventions for this patient group, which may include deep brain stimulation (DBS). The World Society for Stereotactic and Functional Neurosurgery has argued that at least two successful randomized controlled trials should be available before DBS treatment for a psychiatric disorder is considered "established." The FDA approved DBS for adults with treatment-resistant OCD under a humanitarian device exemption (HDE) in 2009, which requires that a device be used to manage or treat a condition impacting 8,000 or fewer patients annually in the United States. DBS is currently offered to children ages 7 and older with treatment-resistant dystonia under an HDE. Ethical and empirical work are needed to evaluate whether and under what conditions it might be appropriate to offer DBS for treatment-resistant childhood OCD. To address this gap, we report qualitative data from semi-structured interviews with 25 clinicians with expertise in this area. First, we report clinician perspectives on acceptable levels of evidence to offer DBS in this patient population. Second, we describe their perspectives on institutional policies or protocols that might be needed to effectively provide care for this patient population.
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The literature on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises concerns that these technologies may affect personality, mood, and behavior. We conducted semi-structured interviews with researchers (n = 23) involved in developing next-generation DBS systems, exploring their perspectives on ethics and policy topics including whether DBS/aDBS can cause such changes. The majority of researchers reported being aware of personality, mood, or behavioral (PMB) changes in recipients of DBS/aDBS. Researchers offered varying estimates of the frequency of PMB changes. A smaller majority reported changes in personality specifically. Some expressed reservations about the scientific status of the term 'personality,' while others used it freely. Most researchers discussed negative PMB changes, but a majority said that DBS/aDBS can also result in positive changes. Several researchers viewed positive PMB changes as part of the therapeutic goal in psychiatric applications of DBS/aDBS. Finally, several discussed potential causes of PMB changes other than the device itself.
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BACKGROUND: There has been substantial controversy in the neuroethics literature regarding the extent to which deep brain stimulation (DBS) impacts dimensions of personality, mood, and behavior. OBJECTIVE/HYPOTHESIS: Despite extensive debate in the theoretical literature, there remains a paucity of empirical data available to support or refute claims related to the psychosocial changes following DBS. METHODS: A mixed-methods approach was used to examine the perspectives of patients who underwent DBS regarding changes to their personality, authenticity, autonomy, risk-taking, and overall quality of life. RESULTS: Patients (n = 21) who were enrolled in adaptive DBS trials for Parkinson's disease, essential tremor, obsessive-compulsive disorder, Tourette's syndrome, or dystonia participated. Qualitative data revealed that participants, in general, reported positive experiences with alterations in what was described as 'personality, mood, and behavior changes.' The majority of participants reported increases in quality of life. No participants reported 'regretting the decision to undergo DBS.' CONCLUSION(S): The findings from this patient sample do not support the narrative that DBS results in substantial adverse changes to dimensions of personality, mood, and behavior. Changes reported as "negative" or "undesired" were few in number, and transient in nature.
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Estimulação Encefálica Profunda , Distonia , Tremor Essencial , Doença de Parkinson , Humanos , Estimulação Encefálica Profunda/métodos , Distonia/terapia , Tremor Essencial/terapia , Doença de Parkinson/terapia , Doença de Parkinson/psicologia , Qualidade de VidaRESUMO
As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML's human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.