Juan C. Rojas, Patrick G. Lyons, Kaveri Chhikara, Vaishvik Chaudhari, Sivasubramanium V. Bhavani, Muna Nour, Kevin G. Buell, Kevin D. Smith, Catherine A. Gao, Saki Amagai, Chengsheng Mao, Yuan Luo, Anna K Barker, Mark Nuppnau, Haley Beck, Rachel Baccile, Michael Hermsen, Zewei Liao, Brenna Park-Egan, Kyle A Carey, XuanHan, Chad H Hochberg, Nicholas E Ingraham, William F Parker
{"title":"A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research","authors":"Juan C. Rojas, Patrick G. Lyons, Kaveri Chhikara, Vaishvik Chaudhari, Sivasubramanium V. Bhavani, Muna Nour, Kevin G. Buell, Kevin D. Smith, Catherine A. Gao, Saki Amagai, Chengsheng Mao, Yuan Luo, Anna K Barker, Mark Nuppnau, Haley Beck, Rachel Baccile, Michael Hermsen, Zewei Liao, Brenna Park-Egan, Kyle A Carey, XuanHan, Chad H Hochberg, Nicholas E Ingraham, William F Parker","doi":"10.1101/2024.09.04.24313058","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":"40 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.04.24313058","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
Background Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.
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