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: 10667651
• 负责人: Kayte Kelleher Spector-Bagdady
• 金额: $ 46.8万
• 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/10667651
• 关键词: Address; Area; Artificial Intelligence; Asian; Biotechnology; Black race; Characteristics; Communities; Data; Data Set; Disease; Ensure; Equity; Ethnic Origin; Exclusion; General Practices; Goals; Hispanic; Hospitals; Individual; Industry; Inequity; Interview; Knowledge; Life; Measures; Methodology; Methods; Not Hispanic or Latino; Other Genetics; Patients; Policies; Positioning Attribute; Prevalence; Race; Remunerations; Reporting; Research; Research Personnel; Sampling; Scientific Advances and Accomplishments; Structure; Surveys; Technology; Translational Research; Translations; Writing; cohort; data sharing; data sharing networks; design; ethical, legal, and social implication; evidence base; health disparity; improved; individual patient; innovation; interest; patient response; precision medicine; preference; response; skills; 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.

项目摘要 背景：广泛共享患者数据和生物标本可以在以下方面实现拯救生命的进步： 转化科学专注于精准医疗的医院和商业实体， 其他基因和人工智能技术越来越多地合作， 数据资源。为确保公平获取科学进步，数据集必须包括 患者反映疾病的人口分布。然而，我们之前的研究 这表明，许多患者对医院与患者共享他们的数据感到不安。 行业和个人谁确定为黑人或西班牙裔一贯更有可能 报告不适。我们需要测量不同病人对医院数据的反应- 分享实践的权力，以区分不同种族，民族和其他 相关的尺度，以确定有希望的妥协领域，并产生公平的影响。方法： 该提案的目标是确定患者和医院之间的妥协领域， 以尊重患者个人方式改善与行业的数据共享实践 在不同社区之间实现自治和公平的影响。研究目的：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， 与商业实体的实践，以及他们的行业和患者关系，（2）描述 已接触、感兴趣和/或已共享的医院类型 患者数据与行业，并衡量可能推动变革的患者影响类型， 具体的数据共享实践，（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实践的预期影响 一般来说，这是一种习俗，具体来说，也是一种种族和民族习俗。影响：患者不适， 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-这是一个重要的问题， 翻译科学的进步。虽然我们的混合方法ELSI方法来发现区域 医院和病人之间的妥协，而集中那些历史上被排除在外，这 建议将对改善数据共享做法产生重大影响， 用于支持高影响力转化科学的数据。