Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research. (16th August 2018)

• Record Type: Journal Article
• Authors: Puskarich, Michael A.; Callaway, Clif; Silbergleit, Robert; Pines, Jesse M.; Obermeyer, Ziad; Wright, David W.; Hsia, Renee Y.; Shah, Manish N.; Monte, Andrew A.; Limkakeng, Alexander T.; Meisel, Zachary F.; Levy, Phillip D.
• Editors: Newgard, Craig
• Is Part Of: Academic emergency medicine. Volume 26:Number 1(2019)
• Page Start: 97
• Page End: 105
• Publication Date: 2018-08-16
• Subjects: Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1111/acem.13520
• Languages: English
• ISSNs: 1069-6563

Abstract

Abstract: For a variety of reasons including cheap computing, widespread adoption of electronic medical records, digitalization of imaging and biosignals, and rapid development of novel technologies, the amount of health care data being collected, recorded, and stored is increasing at an exponential rate. Yet despite these advances, methods for the valid, efficient, and ethical utilization of these data remain underdeveloped. Emergency care research, in particular, poses several unique challenges in this rapidly evolving field. A group of content experts was recently convened to identify research priorities related to barriers to the application of data science to emergency care research. These recommendations included: 1) developing methods for cross‐platform identification and linkage of patients; 2) creating central, deidentified, open‐access databases; 3) improving methodologies for visualization and analysis of intensively sampled data; 4) developing methods to identify and standardize electronic medical record data quality; 5) improving and utilizing natural language processing; 6) developing and utilizing syndrome or complaint‐based based taxonomies of disease; 7) developing practical and ethical framework to leverage electronic systems for controlled trials; 8) exploring technologies to help enable clinical trials in the emergency setting; and 9) training emergency care clinicians in data science and data scientists in emergency care medicine. The background, rationale, and conclusions of these recommendations are included in the present article.