Ethical Perspectives Towards Using Smart Contracts for Patient Consent and Data Protection of Digital Phenotype Data in Machine Learning Environments

在机器学习环境中使用智能合约获得患者同意和数字表型数据数据保护的伦理视角

• 批准号: 10599498
• 负责人: JOHN David HERRINGTON
• 金额: $ 32.35万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-28 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10599498
• 关键词: Academia; Address; Advocate; Anthropology; Architecture; Artificial Intelligence; Behavioral; Behavioral Sciences; Bioethics; Caregivers; Categories; Characteristics; Clinical; Collaborations; Complex; Consent; Contracts; Data; Data Collection; Data Protection; Data Science; Development; Diagnostic; Disclosure; Early Diagnosis; Economic Policy; Economics; Emerging Technologies; Emotional; Emotions; Ensure; Environment; Ethical Issues; Ethics; Ethnography; Fostering; Future; Health; Health Insurance Portability and Accountability Act; Healthcare; Human; Incentives; Industry; International; Internet of Things; Interview; Knowledge; Language; Laws; Legal; Link; Machine Learning; Maps; Medical; Medical Device; Methods; Modeling; Modernization; Participant; Patient Rights; Patients; Pattern; Perception; Pharmacologic Substance; Phenotype; Policies; Privacy; Psyche structure; Psychiatry; Psychometrics; Research; Research Personnel; Rewards; Risk; Science; Self Determination; Shapes; Specific qualifier value; System; Technology; Transact; United States National Institutes of Health; Vision; Work; behavior measurement; behavioral economics; blockchain; computer program; computerized data processing; data access; data ecosystem; data exchange; data sharing; design; digital; educational atmosphere; health data; individual patient; innovation; insight; interest; lens; new technology; novel; parent grant; parent project; patient oriented; patient privacy; personalized medicine; personalized predictions; phenotypic data; preference; recruit; social; social media; stakeholder perspectives; success; tool; treatment response

PROJECT SUMMARY Our parent project (NIH R01MH125958) is characteristic of a growing number of studies collecting highly gran- ular and sensitive digital phenotyping (DP) data with great promise to advance science and healthcare. DP data, in combination with artificial intelligence and machine learning (AI/ML) is poised to revolutionize clinical ap- proaches to personalized medicine both within and outside of psychiatry. However, DP data also poses potential risks to patients’ privacy and self-determination due to their growing capacity to reveal – and increasingly to predict – emotional and behavioral states undetected by humans. Existing legal protections do not adequately address novel features of the DP data ecosystem which enable applications (including monetization and ex- change of DP data) that are not directly linked to patient benefit and potentially expose patients to risks that are difficult to predict. The objective of this proposal is to identify practical, ethical and technical benefits, challenges and incentives for implementing smart contracts: an emerging privacy-by-design technology with promise to enhance patient control over future uses of their DP data. Our supplement brings together interdisciplinary ex- pertise in bioethics, medical anthropology, decision science/behavioral economics and machine learning . Led by an expert in qualitative and mixed methods, our team will conduct interviews to identify diverse stakeholders’ perceptions and technical understandings about potential benefits, challenges, and ethical considerations for using smart contracts to implement patient data sharing preferences for DP data collected for psychiatric re- search (Aim 1). Informed by Aim 1 findings and using behavioral economics insights, in Aim 2 we will model an optimal “choice architecture” to incentivize diverse stakeholders’ engagement with smart contracts in ways that are ethically justified and align with stakeholder interests. This contribution is significant because it will provide knowledge critical to support collaborations that modernize patient protections within a growing DP data ecosys- tem characterized by complex social, economic, technical and legal relations. Our approach is innovative in that it treats the DP data ecosystem as an ethnographic space comprised of relations between and among human actors as well as technologies emerging in parallel but in the absence of an incentivizing patient -centered frame- work. The work is feasible because our team of established investigators have expertise in ethical , technical, behavioral and policy issues related to DP data use and a track record of success working together on large collaborative projects addressing ethical issues related to emerging technologies including AI/ML.

项目总结