Journal of the Society for Clinical Data Management最新文献

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Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based Application in Multisite Cancer Clinical Trials. 在多站点癌症临床试验中，使用可信的基于云的应用程序将数据从电子健康记录自动输入到电子数据捕获。

Journal of the Society for Clinical Data Management Pub Date : 2025-01-01 Epub Date: 2025-01-14 DOI: 10.47912/jscdm.371

Keith Goodman, Chris Cook, Dani Weatherbee, Sunita Yadav, Dinesh Pal Mudaranthakam, Rachael Sexton, Nichole Mahaffey, Leslie Garcia, Teresa Ta, Erin Cebula, Janie Smart, Tanya Goudeau, Sandy Annis, Aubrey Gilmore, Daria Chugina, Pasarlai Ahmadzai, Antje Hoering, Michael LeBlanc

{"title":"Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based Application in Multisite Cancer Clinical Trials.","authors":"Keith Goodman, Chris Cook, Dani Weatherbee, Sunita Yadav, Dinesh Pal Mudaranthakam, Rachael Sexton, Nichole Mahaffey, Leslie Garcia, Teresa Ta, Erin Cebula, Janie Smart, Tanya Goudeau, Sandy Annis, Aubrey Gilmore, Daria Chugina, Pasarlai Ahmadzai, Antje Hoering, Michael LeBlanc","doi":"10.47912/jscdm.371","DOIUrl":"10.47912/jscdm.371","url":null,"abstract":"Introduction: For more than two decades, researchers have sought to develop and to improve technologies to seamlessly move data from electronic health record (EHR) systems to study forms in electronic data capture (EDC) systems. The goal is to utilize advancing technology to improve study data accuracy and quality while decreasing the burden of data collection on busy clinical research professionals.Objectives: This report discusses findings from the SWOG Cancer Research Network's use of a secure and trusted third-party cloud-based application as a technology link between EHR databases and the clinical trial EDC application. The objectives are to ease the burden on site staff and improve data accuracy and completeness.Methods: Three SWOG sites used a cloud-based EHR-to-EDC application to enter study data for two follow-up tumor assessment case report forms for six patients each. This software-assisted method was compared to manual medical record abstraction. Time savings, error rate, and interrater reliability were measured.Results: A comparison of the two methods demonstrated substantial time savings and improvements in data quality. This is especially true for data fields that can be automatically captured using the application for clinical trials using the software-assisted approach.Conclusions: Using a secure and trusted cloud-based application to access the EHR to assist in data collection for clinical trials resulted in welcome time savings for clinical research professionals and higher data accuracy.","PeriodicalId":520890,"journal":{"name":"Journal of the Society for Clinical Data Management","volume":"5 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinical Data Warehousing: A Scoping Review. 临床数据仓库：范围审查。

Journal of the Society for Clinical Data Management Pub Date : 2024-01-01 Epub Date: 2024-08-28 DOI: 10.47912/jscdm.320

Zhan Wang, Mahanaz Syed, Shorabuddin Syed, Melody Greer, Emel Seker, Meredith N Zozus, Catherine K Craven

{"title":"Clinical Data Warehousing: A Scoping Review.","authors":"Zhan Wang, Mahanaz Syed, Shorabuddin Syed, Melody Greer, Emel Seker, Meredith N Zozus, Catherine K Craven","doi":"10.47912/jscdm.320","DOIUrl":"10.47912/jscdm.320","url":null,"abstract":"Introduction: A clinical data warehouse (CDW) is a powerful resource that supports clinical decision-making and secondary data use by integrating and presenting heterogeneous data sources. Despite considerable effort within healthcare organizations (HCOs) to develop CDWs, scientific literature surrounding clinical data warehousing methods is limited.Objectives: The scoping review aims to characterize the current state of CDW methods within HCOs, to identify extant evidence for practice recommendations, and ultimately to advance the design, implementation, and use of CDWs.Methods: The review encompasses CDW articles published from 2011 through 2021 identified through a systematic PubMed search. Article abstracts were systematically screened by two authors. Full-text articles were reviewed and abstracted independently by two authors with discrepancies resolved through consensus.Results: 137 articles, from 55 journals and 3 conference proceedings, were categorized and analyzed. Areas for increased CDW focus include CDW design (such as data integration of increased data types and sources; extract-transform-load (ETL) optimization; data quality improvement processes; semantic data representation) and CDW governance (such as support tools/documentation and data literacy efforts for staff and end-users; governance structure; and business model/financial support for CDWs including staffing).Conclusion: The study indicates the topics that have been significantly developed and the aspects that need additional focus and reporting in CDW between existing general data management best practices and recently articulated requirements for research data. Also, more multi-site and multi-aspect studies are needed to foster maturity at CDWs.","PeriodicalId":520890,"journal":{"name":"Journal of the Society for Clinical Data Management","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geographic Information Systems as Data Sharing Infrastructure for Clinical Data Warehouses. 地理信息系统作为临床数据仓库的数据共享基础设施。

Journal of the Society for Clinical Data Management Pub Date : 2023-01-01 DOI: 10.47912/jscdm.240

Daniel R Harris

{"title":"Geographic Information Systems as Data Sharing Infrastructure for Clinical Data Warehouses.","authors":"Daniel R Harris","doi":"10.47912/jscdm.240","DOIUrl":"10.47912/jscdm.240","url":null,"abstract":"Introduction: The sharing of patient location data is heavily regulated by laws that protect patient privacy and by institutional review boards that are designed to protect the rights and wellbeing of human subjects. Patient location data has incredible value for research due to environmental social determinants of health that heavily influence patient outcomes. The misalignment of sensitivity and utility creates a barrier for researchers that wish to understand the impact of neighborhood-level social determinants of health (SDOH) on health outcomes.Objectives: We describe data warehousing solutions for the safe and ethical enhancement of patient location data; these enhancements will enable easier data sharing.Methods: We deploy geographic information systems (GIS) as a complementary data warehousing service that imports patient addresses, processes the data, and returns results back to the warehouse for others to use and disseminate.Results: We processed 1.3 million patients and added facets about their location to our data warehouse, where they may later be requested for research. We further added de-identified information about the demographics of patient locations from the United States Census Bureau, which assists researchers in understanding the impact of location-driven SDOH.Conclusions: GIS support is a necessary component of a clinical data warehouse and sharing derived, de-identified data is both feasible and useful for research. Open-source software lowers the barrier of adoption and the cost associated with deriving these contextual data points.","PeriodicalId":520890,"journal":{"name":"Journal of the Society for Clinical Data Management","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147944492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0