The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective

Potier, Rémy

doi:10.3389/fpsyg.2020.01218

Cited by 13 publications

(11 citation statements)

References 159 publications

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“…The clinical use of digital phenotyping tools is also thought to have the potential to disrupt the traditional patient-therapist relationship. Artificial intelligence tools are thought to have the potential to disrupt or even replace some of the roles traditionally held by therapists or clinicians [82][83][84]. The use of artificial intelligence methods, such as machine learning and natural language processing, is thought to raise issues of whether the device's findings will be viewed by physicians and patients as more objective than as physician judgment or patient's self-report, thus intruding upon the therapeutic relationship.…”

Section: Introductionmentioning

confidence: 99%

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Martinez-Martin¹,

Greely²,

Cho³

2021

JMIR Mhealth Uhealth

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Background Digital phenotyping (also known as personal sensing, intelligent sensing, or body computing) involves the collection of biometric and personal data in situ from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. Objective This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. Methods We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. Results The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. Conclusions The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

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Section: Introductionmentioning

confidence: 99%

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Martinez-Martin¹,

Greely²,

Cho³

2021

JMIR Mhealth Uhealth

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“…Network analysis helps to determine if one domain or function is more important than others, if changes in one domain, factor, or symptom dynamically influence the function of others, and finally, what factor or domain is driving the network. In sum, network analysis will not only indicate which domains and factors mostly define individual vulnerability but will have important implications for clinical practice motivating personalized strategies in the prevention of mood, behavioral, and cognitive problems (46,(54)(55)(56)(57).…”

Section: Dynamic Mapping Of Multidimensional Datamentioning

confidence: 99%

Digital Phenotyping and Dynamic Monitoring of Adolescents Treated for Cancer to Guide Intervention: Embracing a New Era

et al. 2021

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“…These were 1) Google Analytics data drawn from web-search histories, 2) lifestyles or social determinants of health data (names used interchangeably in Opioid360's promotions) which originated in purchased and proprietary records of insurance claims, consumer transactions, and social media posts, and 3) public datasets including population health and demographic surveys (see Figure 1). Extending digital phenotyping imaginaries, Opioid360's presentation appealed to the notion that comprehensive personal data can offer to behavioural science the precision that genomics has offered to the identification of rare diseases (Potier, 2020). The volume of this data was made relatable to health and medical audiences with comparisons between established methods of clinical and public health risk identification.…”

Section: Promises Of Total Visionmentioning

confidence: 99%

Social determinants of health in the Big Data mode of population health risk calculation

Rowe

2021

Big Data & Society

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Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ (SDOH) in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely.

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The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective

Cited by 13 publications

References 159 publications

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Digital Phenotyping and Dynamic Monitoring of Adolescents Treated for Cancer to Guide Intervention: Embracing a New Era

Social determinants of health in the Big Data mode of population health risk calculation

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