Hospitals Sharing Patient Data and Biospecimens with Commercial Entities: Evidence-Based Translation to Improved Practice

医院与商业实体共享患者数据和生物样本：基于证据的翻译以改进实践

• 批准号: 10501505
• 负责人: Kayte Kelleher Spector-Bagdady
• 金额: $ 46.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10501505
• 关键词: Address; Area; Artificial Intelligence; Asian; Biotechnology; Black race; Characteristics; Communities; Data; Data Set; Disease; Ensure; Ethnic Origin; General Practices; Goals; Hispanic; Hospitals; Individual; Industry; Interview; Knowledge; Life; Measures; Methodology; Methods; Not Hispanic or Latino; Other Genetics; Patients; Policies; Positioning Attribute; Prevalence; Race; Remunerations; Reporting; Research; Research Personnel; Sampling; Savings; Scientific Advances and Accomplishments; Structure; Surveys; Technology; Translational Research; Translations; cohort; data sharing; data sharing networks; design; ethical legal social implication; evidence base; health disparity; improved; individual patient; innovation; interest; patient response; precision medicine; preference; response; translational impact

PROJECT SUMMARY ABSTRACT Background: Widely sharing patient data and biospecimens can enable life-saving advances in translational science. Hospitals and commercial entities focusing on precision medicine and other genetic and artificial intelligence technologies have increasingly partnered to share these data resources. To ensure equitable access to scientific advances, datasets must include patients reflecting the demographic distribution of disease. However, our previous research demonstrates that many patients are uncomfortable with hospitals sharing their data with industry and that individuals who identify as Black or Hispanic are consistently more likely to report discomfort. We need to measure diverse patient responses to actual hospital data- sharing practices with the power to differentiate preferences across race, ethnicity, and other relevant scales to identify promising areas for compromise with an equitable impact. Approach: The goal of this proposal is to identify areas for compromise between patients and hospitals to improve data-sharing practices with industry in ways that are respectful of individual patient autonomy and equitable in impact across diverse communities. Research Aims: To achieve this goal, our project has three specific aims: (1) Explore hospital data-sharing policies and practices with commercial entities, and their industry and patient relationships, (2) Characterize the kinds of hospitals that have been approached by, are interested in, and/or have shared patient data with industry and measure the kinds of patient impact that might drive change to specific data-sharing practices, and (3) Examine the acceptability of hospital data-sharing practices, preferences amongst acceptable practices, and anticipated impact of unacceptable practices generally as well as by race and ethnicity specifically. Impact: Patient discomfort with hospital data sharing practices that patients find unacceptable – and its association with race and ethnicity – is a significant problem for increasing the accessibility and generalizability of translational science advances. Though our mixed methods ELSI approach to discovering areas for compromise between hospitals and patients, while centering those historically excluded, this proposal will have a major impact on improving data-sharing practices to facilitate diversity in data used to support high-impact translational science.

项目总结摘要 背景：广泛共享患者数据和生物标本可以在 翻译科学。医院和商业实体专注于精准医疗和 其他遗传和人工智能技术越来越多地合作共享这些 数据资源。为了确保公平地获得科学进步，数据集必须包括 反映疾病人口分布的患者。然而，我们之前的研究 表明许多患者对医院与其共享数据感到不舒服 而那些认为自己是黑人或西班牙裔的人一贯更有可能 报告不适。我们需要衡量患者对实际医院数据的不同反应- 分享实践，能够区分不同种族、民族和其他人的偏好 相关的比例尺，以确定具有公平影响的有希望的折衷领域。方法： 这项提案的目标是确定患者和医院之间可以妥协的领域，以 以尊重患者个人的方式改进与业界的数据共享实践 在不同社区中的自主性和公平影响。研究目标：实现 为了实现这一目标，我们的项目有三个具体目标：(1)探索医院数据共享政策和 与商业实体的实践，及其行业和患者关系，(2)特点 已接触、感兴趣和/或已共享的医院类型 患者数据与行业，并衡量可能推动变化的患者影响的种类 具体的数据共享实践，以及(3)检查医院数据共享的可接受性 实践、可接受实践中的偏好以及不可接受的预期影响 一般做法以及具体按种族和族裔划分。影响：患者对以下各项感到不适 患者认为不可接受的医院数据共享做法--及其与种族的关联 和种族--是提高可获得性和普遍性的一个重大问题 翻译科学取得了进步。通过我们的混合方法ELSI方法来发现区域 对于医院和患者之间的妥协，当以那些历史上被排除在外的人为中心时，这 该提案将对改善数据共享做法产生重大影响，以促进 用于支持高影响力翻译科学的数据。