2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, 
and reproduction in any medium, provided the original work is properly cited.

ISDS 2014 Conference Abstracts

Evaluation of Praedico™, A Next Generation Big Data 
Biosurveillance Application
Mark Holodniy*1, Carla Winston1, Cynthia A. Lucero-Obusan1, Gina Oda1, Anosh 
Mostaghimi1, Julie A. Pavlin3, Payam Etminani2, Chris Lee2 and Farshid Sedghi2

1Office of Public Health, Department of Veterans Affairs, Palo Alto, CA, USA; 2Bitscopic Inc., Redwood City, CA, USA; 3Armed Forces 
Health Surveillance Center, Silver Spring, MD, USA

Objective
The purpose of our study was to conduct an initial assessment of 

the biosurveillance capabilities of a new software application called 
Praedico™ and compare results obtained from previous queries with 
the Electronic Surveillance System for the Early Notification of 
Community-Based Epidemics (ESSENCE).

Introduction
The National Strategy for Biosurveillance promotes a national effort 

to improve early detection and enable ongoing situational awareness 
of all-hazards threats. Implicit in the Strategy’s implementation plan is 
the need to upgrade capabilities and integrate multiple disparate data 
sources, including more complete electronic health record (EHR) data 
into future biosurveillance capabilities. Thus, new biosurveillance 
applications are clearly needed. Praedico™ is a next generation 
biosurveillance application that incorporates cloud computing 
technology, a Big Data platform utilizing MongoDB as a data 
management system, machine-learning algorithms, geospatial and 
advanced graphical tools, multiple EHR domains, and customizable 
social media streaming from public health-related sources, all within 
a user friendly interface.

Methods
Initially, 1 million VA patient records were analyzed as a training 

set for validation of the Extract-Transform-Load (ETL) layer into a 
designated biomart within the VA Healthcare-Associated Infection 
and Influenza Surveillance System (HAIISS) and analyzed for 
utilization, processivity, searchability, display functions, timing 
and accuracy. In addition, a validation set of combined VA and 
Department of Defense (DoD) biosurveillance data comprising 17 
million DoD and 25 million VA records respectively, was used to 
assess the performance of Praedico™ using ICD-9 encounter codes 
from known influenza-like illness (ILI) outpatient visits previously 
analyzed using ESSENCE alone1.

Results
Utilizing a big data NoSQL database backend, Praedico™ required 

significantly less data storage and utilization, while providing the 
same analytical capabilities. Praedico™ was flexible in terms of 
hardware and software requirements using a modular design that 
could run on commodity hardware and any major operating system 
including Linux, Windows and OS X. Using a single operation in 
Praedico™, the number of ILI encounters in VA and DOD as a 
proportion of the total number of cases during that same 2007-2008 
time period (Figure 1) could be queried and displayed in seconds. 
Praedico™ dashboard also geographically displayed the results for 
both VA and DoD on a single map using a geospatial tool embedded 
directly in the application. Both applications displayed the same 
number of VA ILI cases. Additionally, new ILI queries could be run 
within the Praedico™ dashboard and updated results displayed in real 
time.

Conclusions
Our analysis demonstrates improved resource utilization and 

response time for Praedico™, and results were comparable to VA 
ESSENCE.

Figure 1. Praedico dashboard displaying VA and DoD ILI Data

Keywords
biosurveillance; Veterans; Big Data; system architecture; ESSENCE

Acknowledgments

The findings and conclusions in this report are those of the authors and 
do not reflect the official position of the U.S. Departments of Defense and 
Veterans Affairs 

References

1. Pavlin JA, et al. Combining Surveillance Systems: Effective Merging of
U.S. Veteran and Military Health Data. PLoS ONE 2013 epub Dec 26.

*Mark Holodniy
E-mail: mark.holodniy@va.gov    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e133, 201