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ISDS 2016 Conference Abstracts

Measuring and Improving the Quality of Data Used for 
Syndromic Surveillance
Brian E. Dixon*1, 2, 3, Jon Duke4, 1 and Shaun Grannis1, 5
1Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, USA; 2Indiana University Richard M. Fairbanks 
School of Public Health, Indianapolis, IN, USA; 3Department of Veterans Affairs, Health Services Research & Development Service, 
Indianapolis, IN, USA; 4Georgia Tech Research Institute, Atlanta, GA, USA; 5Indiana University School of Medicine, Indianapolis, IN, 
USA

Objective
To extend an open source platform for measuring the quality 

of electronic health data by adding functions useful for syndromic 
surveillance.

Introduction
Nearly all of the myriad activities (or use cases) in clinical and 

public health (e.g., patient care, surveillance, community health 
assessment, policy) involve generating, collecting, storing, analyzing, 
or sharing data about individual patients or populations. Effective 
clinical and public health practice in the twenty-first century requires 
access to data from an increasing array of information systems, 
including but not limited to electronic health records. However, the 
quality of data in electronic health record 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 researchers.

Methods
Using the Health Data Stewardship Framework1, we will extend 

Automated Characterization of Health Information at Large-scale 
Longitudinal Evidence Systems (ACHILLES), a software package 
published open-source by the Observational Health Data Sciences 
and Informatics collaborative (OHDSI; www.ohdsi.org) to measure 
the quality of data electronically reported from disparate information 
systems. Our extensions will focus on analysis of data reported 
electronically to public health agencies for disease surveillance. Next 
we will apply the ACHILLES extensions to explore the quality of 
data captured from multiple real-world health systems, hospitals, 
laboratories, and clinics. We will further demonstrate the extended 
software to public health professionals, gathering feedback on the 
ability of the methods and software tool to support public health 
agencies’ efforts to routinely monitor the quality of data received for 
surveillance of disease prevalence and burden.

Results
To date we have mapped key surveillance data fields into the 

OHDSI common data model, and we have transformed 111 million 
syndromic surveillance message segments pertaining to 16.4 million 
emergency department encounters representing 6 million patients 
for importation into ACHILLES. Using these data, we are exploring 
the existing 167 metrics across 16 categories available within 
ACHILLES, including a person (e.g., number of unique persons); 
and observation period (e.g., Distribution of age at first observation 
period). Syndromic surveillance (SS), however, is driven largely 
by monitoring patient stated chief complaints (non-standard free 
text clinical data) in addition to coded diagnoses. Consequently, 
ACHILLES must be extended to maximally support use in analyzing 
SS datasets.

Conclusions
This work remains a work-in-progress. Over the coming year, we 

will not only explore existing ACHILLES constructs using real-world 

public health data but also introduce new functionality to explore 
1) patient demographics; 2) facility and location (e.g., emergency
department where care was delivered); and 3) clinical observations 
(e.g., chief complaint). The design and methods for examining these 
aspects of surveillance data will be included on the poster, and they 
will be made freely available for distribution with a future instance of 
the ACHILLES software. We ultimately envision these tools being 
available for use on platforms such as the CDC’s Biosense – open to 
all local and state health agencies as a one-stop portal for surveillance 
data analysis – or research environments where they can be used to 
examine and improve the quality of data output from informatics 
systems.

Keywords
Data quality; Syndromic surveillance; Electronic health records

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 * 9(1):e45, 2017