The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database.
AffiliationUniv Arizona, Coll Engn
Univ Arizona, Coll Med Tucson
Univ Arizona, Dept Med, Div Pulm Allergy Crit Care & Sleep
Univ Arizona, Dept Emergency Med
Univ Arizona, Dept Syst & Ind Engn
Univ Arizona, Dept Biomed Engn
MetadataShow full item record
PublisherJMIR PUBLICATIONS, INC
CitationEssay P, Shahin TB, Balkan B, Mosier J, Subbian V The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database JMIR Med Inform 2019;7(1):e13006 URL: https://medinform.jmir.org/2019/1/e13006 DOI: 10.2196/13006 PMID: 30679148 PMCID: 6365875
JournalJMIR MEDICAL INFORMATICS
Rights© Patrick Essay, Tala B Shahin, Baran Balkan, Jarrod Mosier, Vignesh Subbian. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.01.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractBackground: Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. Objective: The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. Methods: Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. Results: The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. Conclusions: Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.
NoteOpen access journal
VersionFinal published version
SponsorsOffice of Research, Discovery, Innovation at the University of Arizona; National Science Foundation ; National Heart, Lung, and Blood Institute of the National Institutes of Health [5T32HL007955]
Except where otherwise noted, this item's license is described as © Patrick Essay, Tala B Shahin, Baran Balkan, Jarrod Mosier, Vignesh Subbian. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.01.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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