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
基本信息
- 批准号:10591562
- 负责人:
- 金额:$ 65.52万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAdvanced Malignant NeoplasmAffectAgeAlgorithmsAmericanAreaArtificial IntelligenceAttitudeAwarenessBeliefBioethicsCOVID-19CaregiversCaringCessation of lifeChronic DiseaseClinicalCommunicationCommunitiesComputersConsensusDataData SetDecision MakingDementiaDepersonalizationDiseaseElectronic Health RecordEligibility DeterminationEnsureEthicsFamilyFamily CaregiverFrightFutureGenderGeneral PopulationGoalsHealthHealth Care CostsHealth ServicesHealthcareHeart failureHumanIndividualInterdisciplinary StudyInterventionInterviewInvestmentsJudgmentKnowledgeLeadLearningLife ExpectancyMalignant NeoplasmsMediatingMedicalMedical ResearchMedicineMethodsMissionNatureNursesPalliative CarePatientsPhysiciansPlayPopulationProbabilityProcessPrognosisPsychologistRaceRecommendationResearchResource AllocationRiskRobotRoleSamplingServicesSiteSocial WorkersSocioeconomic StatusSourceSpiritual careStructureSurveysTechnologyTranslatingUnited StatesValidationalgorithmic biascare providersdistrustend of lifeexperiencehealth care qualityhealth equityhealth inequalitieshigh riskhospice environmentimprovedindividualized medicineinnovationinsightlearning algorithmliteracymarginalized populationmembermortalitymortality riskneglectpatient engagementpatient prognosispreferenceprognosticprognostic toolprognosticationpsychologicracial biasresearch to practicesocialsociocultural determinantstatisticstooltreatment planningvirtualwillingness
项目摘要
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,能够从巨大的数据集学习,包括
电子健康记录和图表注释,以执行通常为人类保留的任务-准备就绪
对医学研究和实践产生巨大影响,包括临终关怀和姑息治疗(EOLPC)。最近
基于人工智能的算法似乎能够准确预测患者的预后或死亡概率。
提前这些算法可以以自动化的方式进行,而无需临床医生的输入,并且它们是
开始从研究走向实践。对于数百万经历过身体检查的美国人来说,
严重和慢性疾病的心理和社会影响,知道预后可以促进早期
获得姑息治疗,并支持符合患者和家属意愿的医疗决策
目标和偏好。然而,人工智能也引起了人们对患者或临床医生失去自主权的担忧。
决策、非个性化或缺乏同理心的护理、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。研究一致表明,
患者和护理人员可能不知道他们的预后,医生的预测通常不准确,
并且某些社会经济地位或种族的患者可能不太了解他们的预后;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因素而异。
因素因此,基于人工智能的解释将如何影响我们对人工智能作用的基本科学理解。
在医疗决策中的预后意识,以支持高质量,目标一致的EOLPC是一个
关键的知识差距。在人工智能在EOLPC中得到更广泛的应用之前,它会扩展到其他用途(例如,虚拟
护士助理和护理机器人),或者成为服务资格的必要证明(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,
实用的道德准则。该项目的目标是确保人工智能的开发和实施,
支持高质量EOLPC的方法。凭借独特的姑息治疗专家团队,人工智能,
生物伦理学和病人参与,我们将:(1)使用半结构化访谈,以获得丰富的见解,
所有EOLPC团队成员、患者和家庭护理人员关于基于AI的
在美国有目的地选择的4个地点进行验证;(2)进行全国代表性的
姑息治疗医生关于使用基于人工智能的
(3)召集一个德尔菲专家小组,为使用
EOLPC中的AI。该项目将得到姑息治疗研究合作组(PCRC)的支持。
(U2C NR 014637),一个强大的跨学科研究社区,由500多名成员组成,
超过180个网站
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Matthew Wayne DeCamp其他文献
Matthew Wayne DeCamp的其他文献
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{{ truncateString('Matthew Wayne DeCamp', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 65.52万 - 项目类别:
REACH-OUT (Research, Engagement and Action on COVID-19 Health Outcomes via Testing)
REACH-OUT(通过测试对 COVID-19 健康结果进行研究、参与和行动)
- 批准号:
10545080 - 财政年份:2022
- 资助金额:
$ 65.52万 - 项目类别:
REACH-OUT (Research, Engagement and Action on COVID-19 Health Outcomes via Testing)
REACH-OUT(通过测试对 COVID-19 健康结果进行研究、参与和行动)
- 批准号:
10447388 - 财政年份:2022
- 资助金额:
$ 65.52万 - 项目类别:
Patient-Centered Health Reform: Designing Engagement Interventions for ACOs
以患者为中心的医疗改革:为 ACO 设计参与干预措施
- 批准号:
8805062 - 财政年份:2014
- 资助金额:
$ 65.52万 - 项目类别: