Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.
Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.
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DOI：
10.1093/jamia/ocab217
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发表时间：
2022-03-15
期刊：
影响因子：
--
通讯作者：
Zai A
中科院分区：
文献类型：
--
作者：
Pfaff ER;Girvin AT;Gabriel DL;Kostka K;Morris M;Palchuk MB;Lehmann HP;Amor B;Bissell M;Bradwell KR;Gold S;Hong SS;Loomba J;Manna A;McMurry JA;Niehaus E;Qureshi N;Walden A;Zhang XT;Zhu RL;Moffitt RA;Haendel MA;Chute CG;N3C Consortium;Adams WG;Al-Shukri S;Anzalone A;Baghal A;Bennett TD;Bernstam EV;Bernstam EV;Bissell MM;Bush B;Campion TR;Castro V;Chang J;Chaudhari DD;Chen W;Chu S;Cimino JJ;Crandall KA;Crooks M;Davies SJD;DiPalazzo J;Dorr D;Eckrich D;Eltinge SE;Fort DG;Golovko G;Gupta S;Haendel MA;Hajagos JG;Hanauer DA;Harnett BM;Horswell R;Huang N;Johnson SG;Kahn M;Khanipov K;Kieler C;Luzuriaga KR;Maidlow S;Martinez A;Mathew J;McClay JC;McMahan G;Melancon B;Meystre S;Miele L;Morizono H;Pablo R;Patel L;Phuong J;Popham DJ;Pulgarin C;Santos C;Sarkar IN;Sazo N;Setoguchi S;Soby S;Surampalli S;Suver C;Vangala UMR;Visweswaran S;Oehsen JV;Walters KM;Wiley L;Williams DA;Zai A
为了应对COVID-19，信息学界联合起来，尽可能多地收集临床数据，以描述这种新疾病的特征，并通过协作分析减少其影响。国家COVID队列协作（N3 C）现在是美国历史上最大的公共可用的HIPAA有限数据集，拥有超过640万患者，并证明了100多个组织的合作伙伴关系。我们开发了一个管道，用于使用4个联邦公共数据模型来整合，协调和集中来自56个贡献数据合作伙伴的数据。N3 C数据质量（DQ）审查涉及自动和手动程序。在此过程中，在我们的集中式环境中发现了几个DQ问题，无论是在管道中还是在下游基于项目的分析中。对研究中心的反馈导致许多本地和集中DQ改进。除了公认的DQ发现外，我们还发现了15个与源通用数据模型符合性、人口统计学、COVID测试、条件、遭遇、测量、观察、编码完整性和适用性相关的特征。在56家临床试验机构中，37家临床试验机构（66%）通过这些试验证明存在问题。这37个站点在收到反馈后表现出改进。我们在DQ中遇到了站点间的差异，这对于单独使用联合检查发现具有挑战性。我们已经证明，集中的DQ基准测试揭示了DQ改进的独特机会，这将支持本地和整体的研究分析改进。通过将快速、持续的DQ评估与大量的多站点数据相结合，有可能以其所需的规模和严谨性来支持更细微的科学问题。
In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.
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DOI：
10.1093/jamia/ocab117
发表时间：
2021-09-18
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Jiang G;Dhruva SS;Chen J;Schulz WL;Doshi AA;Noseworthy PA;Zhang S;Yu Y;Patrick Young H;Brandt E;Ervin KR;Shah ND;Ross JS;Coplan P;Drozda JP
通讯作者：
Drozda JP
影响因子：
4.2
作者：
Stapff, Manfred Paul
通讯作者：
Stapff, Manfred Paul
影响因子：
39.2
作者：
Stang, Paul E.;Ryan, Patrick B.;Woodcock, Janet
通讯作者：
Woodcock, Janet
DOI：
10.5334/egems.199
发表时间：
2018-04-13
期刊：
EGEMS (Washington, DC)
影响因子：
--
作者：
Qualls LG;Phillips TA;Hammill BG;Topping J;Louzao DM;Brown JS;Curtis LH;Marsolo K
通讯作者：
Marsolo K
DOI：
10.1093/jamia/ocaa196
发表时间：
2021-03-01
影响因子：
6.4
作者：
Haendel, Melissa A.;Chute, Christopher G.;Gersing, Ken R.
通讯作者：
Gersing, Ken R.