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ISDS 2018 Conference Abstracts

Advanced Visualization and Analysis of Data Quality 
for Syndromic Surveillance Systems
Brian E. Dixon*1, 2, 3, Chen Wen2, Tony French2, Jennifer Williams2 and 
Shaun J. Grannis4, 2
1Epidemiology, Indiana University Fairbanks School of Public Health, Indianapolis, IN, USA; 2Regenstrief Insitute, Indianapolis, IN, 
USA; 3Roudebush VA Medical Center, Health Services Research & Development Service, Indianapolis, IN, USA; 4Indiana University 
School of Medicine, Indianapolis, IN, USA

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.

Keywords
Data quality; Syndromic surveillance; Health information exchange

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.

References
1. Dixon BE, Rosenman M, Xia Y, Grannis SJ. A vision for the systematic 

monitoring and improvement of the quality of electronic health data.
Studies in health technology and informatics. 2013;192:884-8.

*Brian E. Dixon
E-mail: bedixon@regenstrief.org

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 10(1):e28, 2018