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

项目摘要 感知计算（PC）与人工智能和机器学习（AI/ML）相结合， 革命性的临床方法，诊断，个性化治疗（精准医学），症状和外， 接下来是监测、远程医疗/移动的健康以及对各种疾病的初级预防。PC tools 但尚未解决的道德和实践挑战阻碍了负责任的翻译 临床护理。这些挑战源于PC指标的具体、新颖的特征，这些特征不同于传统的 情绪和社会行为的常规测量，因为它们1）代表客观观察，而不是亚 主观引发的状态，可能涉及收集患者可能不知道或不希望收集的数字数据。 与他们的临床医生分享; 2）使用数字设备被动收集数据， 时刻的情绪和行为信息; 3）产生大量的材料（即“大数据”），难以 在个人层面上扩展为可操作的信息;以及4）依靠易于收集和推断的数据 从，邀请其目标可能是利润驱动而不是fidu的商业和其他实体参与， 在医疗保健方面，为了帮助实现这项具有广泛临床影响的技术的潜力， 本研究的目的是确定和预测利益和问题（目标1）;优先考虑这些问题 并评估风险/效益权衡（目标2）;并评估PC整合到临床护理的影响（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;各医学专业的临床医生;患者;和护理人员），以确定高优先级 解释和整合PC结果到临床护理的关注和信息需求。访谈结果 将形成内容，由目标2中的利益相关者专家使用3阶段修改的德尔菲进行评估。在 第一轮，我们将进行一项调查，涉及多标准决策分析，以确认和优先考虑突出的 利益相关者之间的利益和潜在危害。代表性参与者的子集（统计学上 将邀请在随后的两轮会议上召开，每轮会议都包括一次决定会议， 军事和民防资源成果，并产生可操作的解决方案和政策指导方针。在目标3中，我们将与重新合作- 研究人员开发了一个多模式PC工具（NIH R 01 MH 125958），向心理健康临床医生提供视频 和患者摄入量的音频记录，包括PC观察以及标准摄入量测量。Cli- 将要求多个研究中心的医师（与NIH R 01 MH 125958无关）提供其最佳临床 在呈现从音频/视频数据导出的PC度量之前和之后的估计，并评估 他们的可解释性，相关性，适当性和可接受性在认知面试的背景下。Trian- 这些目标的预期结果将有助于具体了解不同利益相关者需要知道什么， 为了理解并将PC指标转化为可操作的临床知识，并将有助于NCAT的 旨在确保将PC指标转化为临床护理的成功和可预测的影响。

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.