An ethical framework-guided metric tool for assessing bias in EHR-based Big Data studies

一种道德框架指导的度量工具，用于评估基于电子病历的大数据研究中的偏差

基本信息

批准号：
10599459
负责人：
Bankole Olatosi
金额：
$ 26.76万
依托单位：
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-09 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10599459
关键词：
Address Administrative Supplement Administrator Area Artificial Intelligence Assessment tool Automation Awareness Behavioral Research Behavioral Sciences Big Data Biomedical Research Communities Consent Country Data Data Collection Data Reporting Data Set Development Diagnosis Discrimination Disease Progression Educational workshop Electronic Health Record Ethical Issues Ethics Ethnic Origin FAIR principles Feedback Funding Future Goals Guidelines HIV Health Health Personnel Healthcare Interview Investments Knowledge Lead Literature Machine Learning Measurement Medicine Mining Ownership Parents Patients Policies Population Population Group Process Public Health Race Records Reporting Research Research Infrastructure Research Project Grants Research Training Resources Risk Socioeconomic Status South Carolina Standardization System Technology Underrepresented Populations United States National Institutes of Health Viral Work algorithm training base clinical decision-making data acquisition data curation data management data repository health data health disparity improved insight interdisciplinary collaboration machine learning algorithm machine learning prediction multiple data sources novel parent grant parent project personalized medicine pilot test privacy protection response social health determinants sound tool

项目摘要

Abstract The emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still under-researched aspects of ethical issues is the risk of potential biases in datasets (e.g., electronic health records [EHR] data) as well as in the data curation and acquisition cycles. Very few EHR data-based studies report bias in datasets, data acquisition and/or mining as an indicator of research quality because of a lack of a standardized measurement tool or metrics to assess bias; few ethical frameworks as a theoretical ground; and limited effective interdisciplinary collaboration that engages ethical experts, professional data curators, data management experts, data repository administrators, healthcare workers, and state agencies in discussions addressing this ethical challenge. Since 2021, we have been funded by NIH (R01AI164947) to develop a machine-learning based predictive model of viral suppression among HIV patients based on EHR and other relevant data from multiple sources in South Carolina. One of the ethical challenges encountered by the parent project is how to assess the potential biases in the curation, acquisition, and processing of EHR data. In response to the NOT-OD-22-065 titled “Administrative supplements for advancing the ethical development and use of AI/ML in biomedical and behavioral sciences”, we propose to develop, refine, and pilot test an ethical framework-guided metric tool for assessing bias in Big Data research using EHR datasets. Specifically, we request support to: 1) conduct a literature/policy review and concept analysis to develop an ethical framework for unbiased and inclusive Big Data research; 2) create and modify a metric tool to assess potential biases in EHR data-based studies via in- depth interviews of key stakeholders of the parent project; and 3) refine and disseminate the metric tool through a community charette workshop among interdisciplinary scholars (ethics experts and disciplinary experts) and key stakeholders (data curators, data management experts, and data repository administrators; healthcare workers; and HIV patients) and pilot test it in the parent project. The proposed study will advance our understanding of bias and equity issues in Big Data research and develop an ethical framework and a metric tool for assessing bias in EHR-based Big Data studies, thus leading to and informing a more nuanced assessment and exploration of bias in practice for the ethical development of Big Data health research beyond the parent project. The metric tool of bias for a Big Data study can be reused as an assessment tool to detect and quantify biases, which may contribute to improving awareness and exploration of this critical ethical challenge. The ethical framework regarding bias challenges in Big Data research may provide insights and guidance for addressing bias issues in other types of Big Data beyond EHR.

抽象的大数据健康研究的出现极大地推动了医学和公共卫生领域的发展但也面临着许多道德挑战，这是道德方面最令人担忧但仍未得到充分研究的方面之一。问题是数据集（例如电子健康记录 [EHR] 数据）以及数据中存在潜在偏差的风险很少有基于 EHR 数据的研究报告数据集、数据采集方面的偏差。和/或由于缺乏标准化的测量工具而将挖掘作为研究质量的指标，或者评估偏见的指标；作为理论基础的道德框架很少；有效的跨学科性有限；涉及道德专家、专业数据管理者、数据管理专家、数据存储库管理员、医护人员和国家机构正在讨论解决这一道德问题自 2021 年以来，我们得到了 NIH (R01AI164947) 的资助，开发了一种基于机器学习的方法。基于 EHR 和来自多个国家的其他相关数据的 HIV 患者病毒抑制预测模型母项目遇到的道德挑战之一是如何评估南卡罗来纳州的资源。 EHR 数据的管理、获取和处理中的潜在偏差作为对 NOT-OD-22-065 的回应。标题为“促进人工智能/机器学习在生物医学和医学领域的道德发展和使用的行政补充” “行为科学”，我们建议开发、完善和试点测试一种道德框架指导的衡量工具具体来说，我们请求支持：1) 进行一项使用 EHR 数据集的大数据研究偏差。文献/政策审查和概念分析，为公正和包容性大发展制定道德框架数据研究；2）创建和修改度量工具，通过以下方式评估基于 EHR 数据的研究中的潜在偏差：对母项目的主要利益相关者进行深度访谈；3) 完善和传播衡量工具通过跨学科学者（道德专家和学科专家）参加的社区专题研讨会专家）和主要利益相关者（数据管理者、数据管理专家和数据存储库管理员；以及艾滋病毒患者），并在母项目中进行试点测试。我们对大数据研究中的偏见和公平问题的理解并制定了道德框架和用于评估基于 EHR 的大数据研究中的偏差的度量工具，从而导致并提供更细致的信息大数据健康研究伦理发展实践中的偏见评估和探索大数据研究的偏差度量工具可以重复用作检测的评估工具。并量化偏见，这可能有助于提高对这一关键道德的认识和探索关于大数据研究中的偏见挑战的道德框架可能会提供见解和挑战。解决 EHR 之外其他类型大数据中的偏见问题的指南。