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: 10591562
• 负责人: Matthew Wayne DeCamp
• 金额: $ 65.52万
• 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/10591562
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Age; Algorithms; American; Area; Artificial Intelligence; Attitude; Awareness; Belief; Bioethics; COVID-19; 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; 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; Spiritual care; Structure; Surveys; Technology; Translating; United States; Validation; algorithmic bias; care providers; distrust; end of life; experience; health care quality; health equity; health inequalities; high risk; hospice environment; improved; 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; sociocultural determinant; 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） - 基于计算机的算法，能够从庞大的数据集中学习，包括 电子健康记录和图表注释，以便执行通常为人类保留的任务 - 极大地影响医学研究和实践，包括临终关怀和姑息治疗（EOLPC）。最近的 基于AI的算法似乎能够准确预测患者的预后或死亡年份的预后 进步。这些算法可以自动化，而无需临床医生的输入，它们是 开始从研究转向实践。对于数百万经历身体的美国人来说 严重和慢性病的心理和社会影响，知道预后可以促进 获得姑息治疗并支持与患者和家庭一致的医疗决策 目标和偏好。但是，AI还引起了人们对患者或临床自治的丧失的担忧 决策，人格化或无情的护理，大致有偏见的算法，不信任“黑匣子” 机器，以及对决策中生存统计的过分强调。研究始终表明 患者和看护者可能没有意识到自己的预后，医生在预测中常常不准确， 并且某些社会经济地位或种族的患者可能不了解自己的预后；然而， 对准确预后的需求可能因疾病状态，个人偏好或其他社会文化而有所不同 因素。这，基于AI的编程将如何影响我们对 医学决策的预后意识以支持高质量，目标一致性EOLPC是一个 批判知识差距。在AI在EOLPC中更广泛地使用之前，扩展到其他用途（例如，虚拟 护士助理和照顾者机器人），或成为有资格的服务的证明（例如，临终关怀）， 迫切需要了解其对以患者和家庭为中心的护理的潜在影响并发展 实用的道德指导。该项目的目的是确保开发和实施AI 支持高质量EOLPC的方式。拥有独特的姑息治疗专家团队，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丰富的见解 所有EOLPC团队成员，患者和家庭护理人员的经验和信念 在美国有意选择的4个地点的预测； （2）指挥全国代表 对使用基于AI的预期收益和挑战的姑息治疗医生的调查 预测; （3）召集一个专家小组，以创建用于使用的实用建议 EOLPC中的AI。该项目将在姑息治疗研究合作集团（PCRC）中得到支持 （U2C NR014637），一个强大的跨学科研究社区完成了500多名成员 超过180个站点。