Creating an initial ethics framework for biomedical data modeling by mapping and exploring key decision points

通过映射和探索关键决策点，为生物医学数据建模创建初始伦理框架

基本信息

批准号：
10250400
负责人：
Diane M Korngiebel
金额：
--
依托单位：
HASTINGS CENTER, INC.
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-02 至 2021-09-03
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10250400
关键词：
Accountability Address Area Artificial Intelligence Big Data Bioethical Issues Bioethics Bioethics Consultants Caring Clinical Communities Data Data Science Data Scientist Data Sources Decision Making Development Electronic Health Record Ensure Ethical Issues Ethical Review Ethics Focus Groups Fostering General Population Health Health Resources Health system Individual Informatics Interview Machine Learning Maps Methods Mobile Health Application Modeling National Health Policy Natural Language Processing Nature Output Patients Persons Play Predictive Analytics Process Qualitative Research Reproducibility Research Research Activity Research Methodology Research Personnel Resource Allocation Role Services Social Environment Strategic Planning Structure System Time Trust United States National Institutes of Health Walking base biomedical data science clinical decision support clinical decision-making data modeling data quality data tools ethical legal social implication genomic data high standard improved individual patient informant interest interoperability meetings model development patient population population health programs public trust tool trend usability

项目摘要

Project Summary Biomedical data science data modeling is relevant to a plethora of informatics research activities, such as natural language processing, machine learning, artificial intelligence, and predictive analytics. As Electronic Health Record systems become more advanced and more mature, with the potential to incorporate a wide and diverse array of data from genomics to mobile health (mHealth) applications, the scope and nature of the biomedical data science questions researchers ask become broader. Concomitantly, the answers to their questions have the potential to impact the care of millions of patients—getting the answers right, proactively, is high stakes. However, in data modeling currently, there is no bioethics framework to guide the process of mapping key decision points and recording the rationale for choices made. Making data modeling decision points, as well as the reasoning behind them, explicit would have a twofold impact on improving biomedical data science by: 1. Enhancing transparency and reproducibility and maximizing the value of data science research and 2. Supporting the ability to assess decision points and rationales in terms of their most crucial ethical ramifications. Research in this area is particularly timely amid the interest in, and enthusiasm for, leveraging Big Data sources in the service of improving patient population health and the health of the general public. The National Institutes of Health (NIH) recently released a strategic plan for data science; there is no better time than now to create an initial bioethical framework to inform common data modeling decision points. The improvements in data quality that will derive from decision point mapping and bioethical review will enhance efforts to apply data models across a range of high-impact areas, from predictive analytics to support clinical decision-making to robust trending models in population health to better inform local, regional, and national health policies and resource allocation. To develop this initial bioethics framework, we will use well- established qualitative research methods (interviews, focus groups, and in-person deliberation) to map the decision points in biomedical data modeling research and document the rationales invoked to support those decisions (Aim 1 key informant interviews); assess those data science decision points and decision-making rationales for their bioethical ramifications (Aim 2 focus groups); and create an initial bioethics data modeling framework (Aim 3 deliberative meeting). This study would be the first to provide a bioethics framework to meet a critical gap in biomedical data modeling activities, where the downstream consequences of developing data models without careful and comprehensive review of ethical issues can be severe. This approach directly supports core scientific values of inclusivity, transparency, accountability, and reproducibility that, in turn, foster trust in biomedical data modeling output and potential applications, whether local, national, or global.

项目概要生物医学数据科学数据建模与大量信息学研究活动相关，例如自然语言处理、机器学习、人工智能和预测分析。健康记录系统变得更加先进和成熟，有可能整合广泛和从基因组学到移动健康（mHealth）应用的各种数据，其范围和性质生物医学数据科学研究人员提出的问题也随之变得更加广泛。问题有可能影响数百万患者的护理——积极主动地获得正确的答案是然而，目前在数据建模中，没有生命伦理学框架来指导这一过程。绘制关键决策点并记录做出选择的理由。观点及其背后的推理，明确将对改善生物医学产生双重影响数据科学： 1. 提高透明度和可重复性，最大化数据科学的价值研究和 2. 支持评估决策点和基本原理的能力由于人们对这一领域的兴趣和热情，这一领域的研究显得尤为及时。利用大数据源来改善患者群体的健康和一般人的健康美国国立卫生研究院 (NIH) 最近发布了一项数据科学战略计划；现在是创建初始生物伦理框架来为通用数据建模决策点提供信息的更好时机。决策点绘图和生物伦理审查将带来数据质量的提高增强数据模型在一系列高影响力领域的应用，从预测分析到支持人口健康的稳健趋势模型的临床决策，以更好地为当地、区域和公众提供信息为了制定这个初步的生物伦理框架，我们将充分利用建立定性研究方法（访谈、焦点小组和面对面审议）来绘制生物医学数据建模研究中的决策点并记录支持这些决策点的基本原理决策（目标 1 关键知情者访谈）；评估这些数据科学决策点和决策制定其生物伦理影响的基本原理（目标 2 焦点小组）并创建初始生物伦理数据模型；框架（目标 3 审议会议）。这项研究将是第一个提供满足生物伦理学框架的研究。生物医学数据建模活动中的一个关键差距，其中开发数据的下游后果如果没有对道德问题进行仔细和全面的审查，这种方法可能会很严重。支持包容性、透明度、问责制和可重复性等核心科学价值观，进而促进对生物医学数据建模输出和潜在应用的信任，无论是本地、国家还是全球。