Measuring and Improving the Quality of Data Used for Syndromic Surveillance

How to Cite

Dixon, B. E., Duke, J., & Grannis, S. (2017). Measuring and Improving the Quality of Data Used for Syndromic Surveillance. Online Journal of Public Health Informatics, 9(1).


ObjectiveTo extend an open source platform for measuring the qualityof electronic health data by adding functions useful for syndromicsurveillance.IntroductionNearly all of the myriad activities (or use cases) in clinical andpublic health (e.g., patient care, surveillance, community healthassessment, policy) involve generating, collecting, storing, analyzing,or sharing data about individual patients or populations. Effectiveclinical and public health practice in the twenty-first century requiresaccess to data from an increasing array of information systems,including but not limited to electronic health records. However, thequality of data in electronic health record systems can be poor or“unfit for use.” Therefore measuring and monitoring data quality isan essential activity for clinical and public health professionals aswell as researchers.MethodsUsing the Health Data Stewardship Framework1, we will extendAutomated Characterization of Health Information at Large-scaleLongitudinal Evidence Systems (ACHILLES), a software packagepublished open-source by the Observational Health Data Sciencesand Informatics collaborative (OHDSI; to measurethe quality of data electronically reported from disparate informationsystems. Our extensions will focus on analysis of data reportedelectronically to public health agencies for disease surveillance. Nextwe will apply the ACHILLES extensions to explore the quality ofdata captured from multiple real-world health systems, hospitals,laboratories, and clinics. We will further demonstrate the extendedsoftware to public health professionals, gathering feedback on theability of the methods and software tool to support public healthagencies’ efforts to routinely monitor the quality of data received forsurveillance of disease prevalence and burden.ResultsTo date we have mapped key surveillance data fields into theOHDSI common data model, and we have transformed 111 millionsyndromic surveillance message segments pertaining to 16.4 millionemergency department encounters representing 6 million patientsfor importation into ACHILLES. Using these data, we are exploringthe existing 167 metrics across 16 categories available withinACHILLES, including a person (e.g., number of unique persons);and observation period (e.g., Distribution of age at first observationperiod). Syndromic surveillance (SS), however, is driven largelyby monitoring patient stated chief complaints (non-standard freetext clinical data) in addition to coded diagnoses. Consequently,ACHILLES must be extended to maximally support use in analyzingSS datasets.ConclusionsThis work remains a work-in-progress. Over the coming year, wewill not only explore existing ACHILLES constructs using real-worldpublic health data but also introduce new functionality to explore1) patient demographics; 2) facility and location (e.g., emergencydepartment where care was delivered); and 3) clinical observations(e.g., chief complaint). The design and methods for examining theseaspects of surveillance data will be included on the poster, and theywill be made freely available for distribution with a future instance ofthe ACHILLES software. We ultimately envision these tools beingavailable for use on platforms such as the CDC’s Biosense – open toall local and state health agencies as a one-stop portal for surveillancedata analysis – or research environments where they can be used toexamine and improve the quality of data output from informaticssystems.
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