title: Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.
abstract: Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department.
authors.affiliation: Wayne State University School of Medicine, Detroit, MI. Electronic address: atjanke@gmail.com.
authors.forename: Alexander T
authors.initials: AT
authors.lastname: Janke
authors.affiliation: Department of Emergency Medicine, University of Michigan, Ann Arbor, MI.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Overbeek
authors.affiliation: Department of Emergency Medicine, University of Michigan, Ann Arbor, MI; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
authors.forename: Keith E
authors.initials: KE
authors.lastname: Kocher
authors.affiliation: Department of Emergency Medicine and Cardiovascular Research Institute, Wayne State University, Detroit, MI.
authors.forename: Phillip D
authors.initials: PD
authors.lastname: Levy
mesh_terms: D035843:Biomedical Research; D057286:Electronic Health Records; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D008490:Medical Informatics / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D018570:Risk Assessment; D014481:United States