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.

项目摘要 感知计算（PC）与人工智能和机器学习（AI/ML）结合 彻底改变了诊断，个性化治疗（精度医学），症状和外的临床方法 来监测，远程医疗/移动健康以及各种疾病的主要预防。 PC工具 迅速扩展但未解决的道德和实践挑战以负责任的翻译方式 进入临床护理。这些挑战源于PC指标的特定新颖特征，这与传统不同 情绪和社会行为的统计衡量是因为它们1）代表客观观察而不是子 - 羞辱的引起状态，可能涉及患者可能不知道或希望的数字数据收集 与临床医生分享； 2）使用数字设备被动地收集数据 动量情感和行为信息； 3）很难产生大量材料（即“大数据”） 在个人层面上扩展到可行的信息； 4）依靠易于收集的数据并进行推断 来自商业和其他实体的邀请参与，其目标可能是利润驱动的，而不是 Ciary，如医疗保健。为了帮助实现这项技术的潜力，具有宽度的临床影响， 这项研究的目的是识别和预测利益和关注（目标1）；优先考虑这些问题 并评估风险/福利权衡（目标2）；并评估将PC整合到临床护理中的影响（AIM 3）。在 AIM 1，我们将对潜水员利益相关者（PC工具的研究人员/开发人员打算进行深入的访谈 改善医疗保健；医学专业的临床医生；患者;和护理人员）确定优先级 将PC调查结果解释和集成到临床护理中的关注和信息需求。面试结果 将使用3相修改的Delphi在AIM 2中的专家利益相关者评估的内容。在 第一轮，我们将进行一项调查，需要进行多标准决策分析，以确认和优先级 专家利益相关者之间的利益和潜在危害。代表参与者的子集（从统计上 定义）将邀请在随后的两轮比赛中召集，每场都涉及一次决定会议 MCDA结果并生成可行的解决方案和政策指南。在AIM 3中，我们将与重新合作 搜索者开发多模式PC工具（NIH R01MH125958），以通过视频为心理健康临床医生 除标准摄入量度外，还涉及PC观察的患者摄入量的音频记录。 cl 将要求多个站点的尼古人（与NIH R01MH125958相关）提供最好的临床 估计与从音频/视频数据得出的PC指标之前和之后进行的估计，并评估 在认知访谈的背景下，它们的解释性，相关性，适当性和可接受性。三角 这些目标的审判结果将为潜水者需要了解的内容提供具体见解。 为了理解和转化PC指标为可行的临床知识，并将为NCAT做出贡献 通过确保将PC指标转化为临床护理的成功和可预测的影响来实现目标。

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