%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 21
%N 5
%P e13047
%T Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support
%A Kirkendall,Eric Steven
%A Ni,Yizhao
%A Lingren,Todd
%A Leonard,Matthew
%A Hall,Eric S
%A Melton,Kristin
%+ Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, United States, 1 513 636 4200, ekirkend@wakehealth.edu
%K decision support systems, clinical
%K clinical decision support
%K real-time systems
%K electronic medical records
%K electronic health records
%K medical records systems, computerized
%K informatics
%K data science
%K information science
%K patient safety
%D 2019
%7 22.05.2019
%9 Viewpoint
%J J Med Internet Res
%G English
%X Background: The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care–related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process. Objective: The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system–generated data and the accurate interpretation of the results. Methods: Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format. Results: In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets. Conclusions: The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized. 
%M 31120022
%R 10.2196/13047
%U http://www.jmir.org/2019/5/e13047/
%U https://doi.org/10.2196/13047
%U http://www.ncbi.nlm.nih.gov/pubmed/31120022