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Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research
Author(s) -
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.
Publication year - 2019
Publication title -
academic emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.221
H-Index - 124
eISSN - 1553-2712
pISSN - 1069-6563
DOI - 10.1111/acem.13520
Subject(s) - medicine , leverage (statistics) , data science , variety (cybernetics) , health care , data collection , big data , medical emergency , computer science , data mining , statistics , mathematics , artificial intelligence , machine learning , economics , economic growth
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.