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:10039527
- 负责人:
- 金额:$ 24.32万
- 依托单位:
- 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 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:AccountabilityAddressAreaArtificial IntelligenceBig DataBioethical IssuesBioethicsBioethics ConsultantsCaringClinicalCommunitiesDataData ScienceData ScientistData SourcesDecision MakingDevelopmentElectronic Health RecordEnsureEthical IssuesEthical ReviewEthicsFocus GroupsFosteringGeneral PopulationHealthHealth ResourcesHealth systemIndividualInformaticsInterviewMachine LearningMapsMethodsMobile Health ApplicationModelingNational Health PolicyNatural Language ProcessingNatureOutputPatientsPersonsPlayPredictive AnalyticsProcessQualitative ResearchReproducibilityResearchResearch ActivityResearch MethodologyResearch PersonnelResource AllocationRoleServicesSocial EnvironmentStrategic PlanningStructureSystemTimeTrustUnited States National Institutes of HealthWalkingbasebiomedical data scienceclinical decision supportclinical decision-makingdata modelingdata qualitydata toolsethical legal social implicationgenomic datahigh standardimprovedindividual patientinformantinterestinteroperabilitymeetingsmodel developmentpatient populationpopulation healthprogramspublic trusttooltrendusability
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.
项目总结
项目成果
期刊论文数量(0)
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Diane M Korngiebel其他文献
Diane M Korngiebel的其他文献
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{{ truncateString('Diane M Korngiebel', 18)}}的其他基金
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
- 批准号:
10250400 - 财政年份:2020
- 资助金额:
$ 24.32万 - 项目类别:
Using Ethics and User-Centered Design to Create Templates for EHR-Mediated Return of Genetic Test Results
使用道德和以用户为中心的设计来创建 EHR 介导的基因检测结果返回模板
- 批准号:
9789346 - 财政年份:2018
- 资助金额:
$ 24.32万 - 项目类别:
Ethically responsible clinical decision support for Lynch Syndrome screening
林奇综合征筛查的道德责任临床决策支持
- 批准号:
8804136 - 财政年份:2014
- 资助金额:
$ 24.32万 - 项目类别:
Ethically responsible clinical decision support for Lynch Syndrome screening
林奇综合征筛查的道德责任临床决策支持
- 批准号:
9298688 - 财政年份:2014
- 资助金额:
$ 24.32万 - 项目类别:
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