A mixed-methods study of the nature, extent and consequences of artificial intelligence (AI) for individualized treatment planning in end-of-life and palliative care (EOLPC)

对人工智能 (AI) 在临终关怀和姑息治疗 (EOLPC) 中个性化治疗计划的性质、程度和后果的混合方法研究

• 批准号: 10367249
• 负责人: Matthew Wayne DeCamp
• 金额: $ 67.56万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-14 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367249
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Age; Algorithms; American; Area; Artificial Intelligence; Attitude; Awareness; Belief; Bioethics; COVID-19; Caregiver research; Caregivers; Caring; Cessation of life; Chronic Disease; Clinical; Communication; Communities; Computers; Consensus; Data; Data Set; Decision Making; Dementia; Depersonalization; Disease; Electronic Health Record; Eligibility Determination; Ensure; Ethics; Family; Family Caregiver; Foundations; Fright; Future; Gender; General Population; Goals; Health; Health Care Costs; Health Services; Healthcare; Heart failure; Human; Individual; Interdisciplinary Study; Intervention; Interview; Investments; Judgment; Knowledge; Lead; Learning; Life Expectancy; Malignant Neoplasms; Mediating; Medical; Medical Research; Medicine; Methods; Mission; Nature; Nurses; Palliative Care; Patients; Physicians; Play; Population; Probability; Process; Prognosis; Psychologist; Race; Recommendation; Research; Resource Allocation; Risk; Robot; Role; Sampling; Services; Site; Social Workers; Socioeconomic Status; Source; Structure; Surveys; Technology; Translating; United States; Validation; algorithmic bias; base; care providers; distrust; end of life; experience; health care quality; health equity; health inequalities; high risk; hospice environment; individualized medicine; innovation; insight; learning algorithm; literacy; marginalized population; member; mortality; mortality risk; neglect; patient engagement; patient prognosis; preference; prognostic; prognostic tool; prognostication; psychologic; racial bias; research to practice; social; social culture; statistics; tool; treatment planning; virtual; willingness

PROJECT ABSTRACT Artificial Intelligence (AI) - computer-based algorithms capable of learning from enormous data sets, including electronic health records and chart notes, in order to carry out tasks typically reserved for humans – is poised to dramatically affect medical research and practice, including end-of-life and palliative care (EOLPC). Recent AI-based algorithms seem capable of accurately predicting a patient’s prognosis or probability of death years in advance. These algorithms can do so in an automated fashion, without the input of clinicians, and they are starting to move from research into practice. For the millions of Americans who experience the physical, psychological, and social effects of severe and chronic illness, knowing a prognosis could promote earlier access to palliative care and to support medical decision-making that is consistent with patients’ and families’ goals and preferences. However, AI also raises concerns about loss of autonomy in patient or clinician decision-making, depersonalized or unempathetic care, racially biased algorithms, distrust of “black box” machines, and an over-emphasis on survival statistics in decision-making. Studies consistently show that patients and caregivers may be unaware of their prognosis, that physicians are often inaccurate in predictions, and that patients of certain socioeconomic statuses or races may be less aware of their prognosis; however, the need for an accurate prognosis may vary by disease state, individual preference, or other sociocultural factors. Thus, how AI-based prognostication will affect our basic scientific understanding of the role of prognostic awareness in medical decision-making in support of high quality, goal concordant EOLPC is a critical knowledge gap. Before AI becomes more widely used in EOLPC, spreads to other uses (e.g., virtual nurse assistants and caregiver robots), or becomes necessary as proof o f eligibility for services (e.g., hospice), there is an urgent need to understand its potential impact on patient- and family-centered care and to develop practical ethics guidance for its use. The goal of this project is to ensure AI is developed and implemented in ways that support high quality EOLPC. With a unique team of experts in palliative care, artificial intelligence, bioethics, and patient engagement, we will: (1) use semi-structured interviews to obtain rich insights into the experiences and beliefs of all EOLPC team members, patients, and family caregivers regarding AI-based prognostication at 4 purposefully chosen sites across the United States; (2) conduct a nationally representative survey of palliative care physicians regarding the anticipated benefits and challenges of using AI-based prognostication; and (3) convene a Delphi panel of experts to create practical recommendations for the use of AI in EOLPC. The project will be supported within the Palliative Care Research Cooperative Group (PCRC) (U2C NR014637), a robust interdisciplinary research community comprised of more than 500 members at more than 180 sites.

项目摘要 人工智能(AI)-能够从海量数据集中学习的基于计算机的算法，包括 电子健康记录和图表笔记，以执行通常为人类保留的任务-已准备就绪 显著影响医学研究和实践，包括临终和姑息治疗(EOLPC)。近期 基于人工智能的算法似乎能够准确地预测患者的预后或死亡概率 前进。这些算法可以以自动的方式完成，而不需要临床医生的输入，而且它们是 开始从研究走向实践。对于数百万体验过体检的美国人来说， 严重和慢性病的心理和社会影响，了解预后可以更早地促进 获得姑息治疗并支持与患者和家属一致的医疗决策 目标和偏好。然而，人工智能也引发了人们对患者或临床医生失去自主性的担忧 决策、非个人化或缺乏同情心的照顾、带有种族偏见的算法、对“黑匣子”的不信任 机器，以及在决策中过度强调生存统计数据。研究一直表明， 患者和照顾者可能不知道他们的预后，医生经常预测不准确， 具有特定社会经济地位或种族的患者可能较少意识到自己的预后；然而， 对准确预后的需求可能因疾病状态、个人喜好或其他社会文化因素而异。 各种因素。因此，基于人工智能的预测将如何影响我们对 支持高质量、目标一致的医疗决策的预后意识EOLPC是一种 关键的知识鸿沟。在人工智能在EOLPC中更广泛地使用之前，扩展到其他用途(例如，虚拟 护士助理和护理员机器人)，或成为必要的服务资格证明(例如，临终关怀)， 迫切需要了解它对以病人和家庭为中心的护理的潜在影响，并发展 为其使用提供实用伦理指导。该项目的目标是确保在 支持高质量EOLPC的方式。拥有一支独特的姑息治疗、人工智能、 生物伦理学和患者参与，我们将：(1)使用半结构化访谈来获得对 所有EOLPC团队成员、患者和家庭照顾者关于基于AI的经验和信念 在全美4个有目的地选择的地点进行预测；(2)进行具有全国代表性的 关于使用人工智能的预期好处和挑战的姑息护理医生的调查 预测；以及(3)召集德尔福专家小组，为使用 EOLPC中的AI。该项目将在姑息治疗研究合作小组(PCRC)内得到支持 (U2C NR014637)，一个强大的跨学科研究社区，由500多名成员组成，网址为 180多个地点。