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使用 ExtractEHR 实现自动化电子健康记录数据提取和整理。

Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.

机构信息

Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA.

Department of Pediatrics, Emory University School of Medicine, Atlanta, GA.

出版信息

JCO Clin Cancer Inform. 2024 Nov;8:e2400100. doi: 10.1200/CCI.24.00100. Epub 2024 Nov 25.

DOI:10.1200/CCI.24.00100
PMID:39586036
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11608624/
Abstract

PURPOSE

Although the potential transformative effect of electronic health record (EHR) data on clinical research in adult patient populations has been very extensively discussed, the effect on pediatric oncology research has been limited. Multiple factors contribute to this more limited effect, including the paucity of pediatric cancer cases in commercial EHR-derived cancer data sets and phenotypic case identification challenges in pediatric federated EHR data.

METHODS

The ExtractEHR software package was initially developed as a tool to improve clinical trial adverse event reporting but has expanded its use cases to include the development of multisite EHR data sets and the support of cancer cohorts. ExtractEHR enables customized, automated data extraction from the EHR that, when implemented across multiple hospitals, can create pediatric cancer EHR data sets to address a very wide range of research questions in pediatric oncology. After ExtractEHR data acquisition, EHR data can be cleaned and graded using CleanEHR and GradeEHR, companion software packages.

RESULTS

ExtractEHR has been installed at four leading pediatric institutions: Children's Healthcare of Atlanta, Children's Hospital of Philadelphia, Texas Children's Hospital, and Seattle Children's Hospital.

CONCLUSION

ExtractEHR has supported multiple use cases, including five clinical epidemiology studies, multicenter clinical trials, and cancer cohort assembly. Work is ongoing to develop Fast Health care Interoperability Resources ExtractEHR and implement other sustainability and scalability enhancements.

摘要

目的

尽管电子健康记录（EHR）数据对成人患者群体的临床研究可能具有变革性影响，但对儿科肿瘤研究的影响有限。多种因素导致这种影响更为有限，包括商业 EHR 衍生的癌症数据集中儿科癌症病例的稀缺性，以及儿科联邦 EHR 数据中表型病例识别的挑战。

方法

ExtractEHR 软件包最初是作为一种改进临床试验不良事件报告的工具而开发的，但已扩展其用例，包括开发多站点 EHR 数据集和支持癌症队列。ExtractEHR 可从 EHR 中进行自定义、自动化的数据提取，当在多家医院实施时，可以创建儿科癌症 EHR 数据集，以解决儿科肿瘤学中非常广泛的研究问题。在 ExtractEHR 数据采集之后，可以使用 CleanEHR 和 GradeEHR 等配套软件包对 EHR 数据进行清理和分级。

结果

ExtractEHR 已在四家领先的儿科机构安装：亚特兰大儿童保健中心、费城儿童医院、德克萨斯儿童健康中心和西雅图儿童保健中心。

结论

ExtractEHR 支持多种用例，包括五项临床流行病学研究、多中心临床试验和癌症队列组装。目前正在努力开发 Fast Health care Interoperability Resources ExtractEHR 并实施其他可持续性和可扩展性增强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/a52ba04cb370/cci-8-e2400100-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/246f0d6771bf/cci-8-e2400100-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/dbae3454c12c/cci-8-e2400100-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/7d9139f02977/cci-8-e2400100-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/a52ba04cb370/cci-8-e2400100-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/246f0d6771bf/cci-8-e2400100-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/dbae3454c12c/cci-8-e2400100-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/7d9139f02977/cci-8-e2400100-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a64/11608624/a52ba04cb370/cci-8-e2400100-g004.jpg

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