DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.

Jose-Franck Diaz-Garelli, Elmer V Bernstam, MinJae Lee, Kevin O Hwang, Mohammad H Rahbar, Todd R Johnson
{"title":"DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.","authors":"Jose-Franck Diaz-Garelli, Elmer V Bernstam, MinJae Lee, Kevin O Hwang, Mohammad H Rahbar, Todd R Johnson","doi":"10.5334/egems.286","DOIUrl":null,"url":null,"abstract":"<p><p>The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge's main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.</p>","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":"32"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659577/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EGEMS (Washington, DC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/egems.286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge's main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.

DataGauge:系统设计和实施重新利用的临床数据质量评估的实用过程
众所周知,重新调整数据用途的危害使数据质量(DQ)评估成为确保有效结果的重要一步,无论采用何种分析方法。然而,对于临床数据的二次使用,没有系统的过程来实施DQ评估。本文介绍了DataGauge,这是一个设计和实施DQ评估的系统过程,用于评估用于特定二次用途的重新调整用途的数据。DataGauge由五个步骤组成:(1)定义信息需求,(2)开发正式的数据需求模型(DNM),(3)使用DNM和DQ理论开发特定目标的DQ评估需求,(4)提取DNM指定的数据,以及(5)根据DQ需求进行评估。DataGauge的主要贡献是将一般的DQ理论和DQ评估方法集成到一个系统的过程中。该流程支持现有电子健康记录特定DQ评估指南的集成和实际实施。DataGauge还提供了一个基于理论的初始指导框架,将DNM与每个DQ维度的DQ测试方法联系起来,以帮助设计DQ评估。该框架可以通过现有的DQ指南进行扩充,以实现系统评估。DataGauge通过定义评估过程,为未来的系统DQ评估研究奠定了基础,能够适应广泛的临床数据集和二次使用。定义DataGauge为新的研究方向奠定了基础,如DQ理论集成、DQ需求可移植性研究、DQ评估工具开发和DQ评估工具包可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信