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Online J Public Health InformOnline J Public Health InformOJPHIOnline Journal of Public Health Informatics1947-2579University of Illinois at Chicago Library  ojphi-10-e2810.5210/ojphi.v10i1.8351ISDS 2018 Conference AbstractsAdvanced Visualization and Analysis of Data Quality for Syndromic
Surveillance Systems Dixon Brian E. *
1,

2,

3
Wen Chen 
2
French Tony 
2
Williams Jennifer 
2
Grannis Shaun J. 
4,

2
1 Epidemiology, Indiana University
Fairbanks School of Public Health, Indianapolis,
IN, USA; 2 Regenstrief Insitute,
Indianapolis, IN, USA;3 Roudebush VA Medical Center, Health Services
Research & Development Service, Indianapolis,
IN, USA; 4 Indiana University
School of Medicine, Indianapolis, IN,
USA* Brian E. Dixon E-mail: bedixon@regenstrief.org30 5 2018 2018 10 1 e28ISDS Annual Conference Proceedings 2018. 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.Keywords 
Data qualitySyndromic surveillanceHealth information exchange
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Objective
To extend an open source analytics and visualization platform for measuring the
quality of electronic health data transmitted to syndromic surveillance systems.

Introduction
Effective clinical and public health practice in the twenty-first century requires
access to data from an increasing array of information systems. However, the quality
of data in these systems can be poor or “unfit for use.” Therefore
measuring and monitoring data quality is an essential activity for clinical and
public health professionals as well as researchers1. Current methods for
examining data quality largely rely on manual queries and processes conducted by
epidemiologists. Better, automated tools for examining data quality are desired by
the surveillance community.

Methods
Using the existing, open-source platform Atlas developed by the Observational Health
Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org), we added new
functionality to measure and visualize the quality of data electronically reported
from disparate information systems. Our extensions focused on analysis of data
reported electronically to public health agencies for disease surveillance.
Specifically, we created methods for examining the completeness and timeliness of
data reported as well as the information entropy of the data within syndromic
surveillance messages sent from emergency department information systems.

Results
To date we transformed 111 million syndromic surveillance message segments pertaining
to 16.4 million emergency department encounters representing 6 million patients into
the OHDSI common data model. We further measured completeness, timeliness and
entropy of the syndromic surveillance data. In Figure-1, the OHDSI tool Atlas
summarizes the analysis of data completeness for key fields in over one million
syndromic surveillance messages sent to Indiana’s health department in 2014.
Completeness is reported by age category (e.g., 0-10, 20-30, 60+). Gender is
generally complete, but both race and ethnicity fields are often complete for less
than half of the patients in the cohort. These results suggest areas for improvement
with respect to data quality that could be actionable by the syndromic surveillance
coordinator at the state health department.

Conclusions
Our project remains a work-in-progress. While functions that assess completeness,
timeliness and entropy are complete, there may be other functions important to
public health that need to be developed. We are currently soliciting feedback from
syndromic surveillance stakeholders to gather ideas for what other functions would
be useful to epidemiologists. Suggestions could be developed into functions over the
next year. We are further working with the OHDSI collaborative to distribute the
Atlas enhancements to other platforms, including the National Syndromic Surveillance
Platform (NSSP). Our goal is to enable epidemiologists to quickly analyze data
quality at scale.

Data completeness for key syndromic surveillance fields summarized across 1
million emergency department messages transmitted in 2014.

Acknowledgments
Research reported in this abstract was supported by the National Library of Medicine
of the National Institutes of Health under Award Number R21LM012219. The content is
solely the responsibility of the authors and does not necessarily represent the
official views of the National Institutes of Health.
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References
1 Dixon BE Rosenman M Xia Y Grannis SJ  .
2013 . A vision for the systematic monitoring and
improvement of the quality of electronic health data .
Stud Health Technol Inform . 192 ,
884 -88 .23920685