Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care

影响感知计算成功转化以改善临床护理的伦理和人为因素

• 批准号: 10680488
• 负责人: JOHN David HERRINGTON
• 金额: $ 48.79万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680488
• 关键词: Address; Anthropology; Area; Artificial Intelligence; Asthma; Awareness; Behavior; Behavioral; Benefits and Risks; Big Data; Bioethics; Bioethics Consultants; Biological Markers; Cancer Detection; Caregivers; Cellular Phone; Classification; Clinical; Clinical assessments; Collaborations; Collection; Communication; Consensus; Data; Decision Analysis; Decision Making; Deterioration; Devices; Diagnosis; Disease; Early Diagnosis; Emotional; Ensure; Ethics; Evaluation; Goals; Guidelines; Healthcare; Heart failure; Human; Image; Individual; Informed Consent; Intake; Interview; Investments; Knowledge; Machine Learning; Maternal Health; Measures; Medical; Medicine; Mental Health; Mental disorders; Methodology; Methods; Monitor; Ophthalmology; Outcome; Participant; Patients; Phase; Policies; Policy Maker; Prediction of Response to Therapy; Primary Prevention; Psychiatric therapeutic procedure; Psychiatry; Psychology; Publications; Quality of Care; Radiology Specialty; Recording of previous events; Research; Research Personnel; Rest; Risk Assessment; Science; Scientist; Site; Social Behavior; Surveys; Symptoms; Technology; Telemedicine; Translating; Translations; United States National Institutes of Health; artificial intelligence algorithm; biobehavior; chronic pain; clinical care; clinical research site; clinically actionable; cognitive interview; computer science; design; digital; disorder subtype; emotional behavior; emotional functioning; experience; high standard; improved; indexing; innovation; insight; mHealth; medical specialties; multimodality; nervous system disorder; novel; patient privacy; patient safety; personalized care; personalized medicine; precision medicine; risk mitigation; stem; success; symposium; tool; wearable device

PROJECT SUMMARY Perceptual computing (PC), in combination with artificial intelligence and machine learning (AI/ML), is poised to revolutionize clinical approaches to diagnosis, personalized treatment (precision medicine), symptom and out- come monitoring, telemedicine/mobile health, and primary prevention across a wide range of disorders. PC tools are rapidly expanding but unresolved ethical and practical challenges stand in the way of responsible translation into clinical care. These challenges stem from the specific, novel features of PC metrics, which differ from tradi- tional measures of emotional and social behavior in that they 1) represent objectively observed rather than sub- jectively elicited states and may involve collection of digital data that patients may not be aware of or wish to share with their clinicians; 2) collect data passively using digital devices that observe and register moment-to- moment emotional and behavioral information; 3) yield voluminous material (i.e. “big data”) that is difficult to scale into actionable information at the individual level; and 4) rest on data easy to collect and make inferences from, inviting engagement from commercial and other entities whose goals may be profit-driven rather than fidu- ciary, as in healthcare. To help realize the potential of this technology with widespread clinical impacts, the objective of this research is to identify and anticipate benefits and concerns (Aim 1); prioritize these concerns and assess risk/benefit tradeoffs (Aim 2); and evaluate impacts of integrating PC into clinical care (Aim 3). In Aim 1, we will conduct in-depth interviews with diverse stakeholders (researcher/developers of PC tools intended to improve healthcare; clinicians across medical specialties; patients; and caregivers) to identify high priority concerns and information needs for interpreting and integrating PC findings into clinical care. Interview findings will form the content to be evaluated by expert stakeholders in Aim 2 using a 3-phase modified Delphi. In the first round, we will conduct a survey entailing Multi-criteria Decision Analysis to confirm and prioritize salient benefits and potential harms among expert stakeholders. A subset of representative participants (statistically defined) will be invited to convene in two subsequent rounds, each involving a Decision Conference to review MCDA results and generate actionable solutions and policy guidelines. In Aim 3, we will collaborate with re- searchers developing a multimodal PC tool (NIH R01MH125958) to present mental health clinicians with video and audio recordings of patient intakes involving PC observations in addition to standard intake measures. Cli- nicians across multiple sites (unaffiliated with NIH R01MH125958) will be asked to provide their best clinical estimates before and after being presented with PC metrics derived from the audio/video data, and to evaluate their interpretability, relevance, appropriateness, and acceptability in the context of a cognitive interview. Trian- gulated results from these aims will contribute concrete insights into what diverse stakeholders need to know in order to understand and translate PC metrics into actionable clinical knowledge and will contribute to NCAT’s aims by ensuring the success and predictable impacts of translating PC metrics into clinical care.

项目概要 感知计算 (PC) 与人工智能和机器学习 (AI/ML) 相结合，有望 彻底改变诊断、个性化治疗（精准医学）、症状和结果的临床方法 包括监测、远程医疗/移动医疗以及各种疾病的初级预防。电脑工具 正在迅速扩张，但尚未解决的道德和实践挑战阻碍了负责任的翻译 进入临床护理。这些挑战源于 PC 指标的具体新颖特征，这些特征不同于传统的指标。 情绪和社会行为的衡量标准，因为它们 1）代表客观观察到的而不是次要的 客观引发的状态，可能涉及收集患者可能不知道或不希望的数字数据 与临床医生分享； 2）使用观察和记录时刻的数字设备被动收集数据 瞬间情绪和行为信息； 3）产生大量难以处理的材料（即“大数据”） 扩展到个人层面的可操作信息； 4）依靠易于收集和推断的数据 邀请商业和其他实体的参与，其目标可能是利润驱动而不是信托 ciary，如医疗保健领域。为了帮助实现这项技术具有广泛临床影响的潜力， 本研究的目标是确定并预测收益和关注点（目标 1）；优先考虑这些问题 并评估风险/收益权衡（目标 2）；并评估将 PC 纳入临床护理的影响（目标 3）。在 目标1，我们将与不同的利益相关者（PC工具的研究人员/开发人员）进行深入访谈 改善医疗保健；跨医学专业的临床医生；患者;和护理人员）以确定高优先级 解释 PC 结果并将其整合到临床护理中的担忧和信息需求。访谈结果 将使用 3 阶段修改的 Delphi 形成目标 2 中由专家利益相关者评估的内容。在 第一轮，我们将进行一项涉及多标准决策分析的调查，以确认并优先考虑显着的问题 专家利益相关者之间的利益和潜在危害。代表性参与者的子集（统计上 定义）将被邀请召开后续两轮会议，每轮都涉及一次决策会议来审查 MCDA 结果并产生可操作的解决方案和政策指南。在目标 3 中，我们将与重新合作 搜索者正在开发多模式 PC 工具 (NIH R01MH125958)，向心理健康临床医生提供视频 除了标准摄入量测量外，还包括患者摄入量的音频记录，涉及 PC 观察。克里- 多个地点的 nicians（与 NIH R01MH125958 无关）将被要求提供他们最好的临床资料 在提供从音频/视频数据派生的 PC 指标之前和之后进行估计，并评估 它们在认知访谈中的可解释性、相关性、适当性和可接受性。特里安- 这些目标的预期结果将为不同利益相关者需要了解的内容提供具体的见解 为了理解 PC 指标并将其转化为可操作的临床知识，并将为 NCAT 做出贡献 目标是确保将 PC 指标转化为临床护理的成功和可预测的影响。

项目成果

AI in the hands of imperfect users.

• DOI: 10.1038/s41746-022-00737-z
• 发表时间: 2022-12-28
• 期刊: NPJ digital medicine
• 影响因子: 15.2

Integrating Social Determinants of Health into Ethical Digital Simulations.

将健康的社会决定因素纳入道德数字模拟。

• DOI: 10.1080/15265161.2023.2237443
• 发表时间: 2023
• 期刊: The American journal of bioethics : AJOB
• 作者: Kostick-Quenet,Kristin;Rahimzadeh,Vasiliki;Anandasabapathy,Sharmila;Hurley,Meghan;Sonig,Anika;Mcguire,Amy
• 通讯作者: Mcguire,Amy

Ethical hazards of health data governance in the metaverse.

• DOI: 10.1038/s42256-023-00658-w
• 发表时间: 2023-05
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Kostick-Quenet, Kristin;Rahimzadeh, Vasiliki
• 通讯作者: Rahimzadeh, Vasiliki