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.

项目摘要生物医学数据科学数据建模与大量信息研究活动有关，例如自然语言处理，机器学习，人工智能和预测分析。作为电子健康记录系统变得更加先进，更成熟，有可能结合范围的从基因组学到移动健康（MHealth）应用程序的各种数据，范围和性质生物医学数据科学问题研究人员提出的问题变得更广泛。同时，他们的答案问题有可能影响数百万患者的护理 - 主动地确定答案是正确的高赌注。但是，在当前数据建模中，没有生物伦理学框架可以指导该过程绘制关键决策点并记录做出选择的理由。做出数据建模决策点以及背后的推理，显式将对改善生物医学产生双重影响数据科学作者：1。提高透明度和可重复性并最大化数据科学的价值研究和2。支持评估决策点和理由的能力道德后果。在对这一领域的研究尤其及时，在对，热情的兴趣和热情中利用大数据源来改善患者人口健康和一般健康状况民众。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；没有比现在更好的时间来创建一个初始的生物伦理框架，以告知通用数据建模决策点。从决策点映射和生物伦理审查中得出的数据质量的改进将从预测分析到支持临床决策到人口健康中强大的趋势模型，以更好地告知本地，地区和国家卫生政策和资源分配。为了开发这个最初的生物伦理学框架，我们将使用良好的建立的定性研究方法（访谈，焦点小组和面对面的审议）来绘制生物医学数据建模研究的决策点并记录了所援引的理由来支持那些决策（AIM 1关键线人访谈）；评估这些数据科学决策要点和决策其生物伦理影响的理由（AIM 2焦点小组）；并创建初始的生物伦理数据建模框架（目标3审议会议）。这项研究将是第一个提供生物伦理学框架来满足的。生物医学数据建模活动中的一个关键差距，在其中开发数据的下游后果没有仔细且全面审查道德问题的模型可能很严重。这种方法直接支持包容性，透明度，问责制和可重复性的核心科学价值，从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。