NSSP Data Quality (DQ) Dashboard demo

The DQ Dashboard is an interactive tool developed to help you identify potential data processing issues and to ensure useful syndromic data by measuring the timeliness, completeness, and validity of data being processed on the BioSense Platform.

September 20, 2019

ESSENCE Q & A v5.0

Held on June 19, 2019.

During this 90-minute session, Aaron Kite-Powell, M.S., from CDC and Wayne Loschen, M.S., from JHU-APL provided updates on the NSSP ESSENCE platform and answered the community's questions on ESSENCE functions and features.

June 20, 2019

Improving Varicella Investigation Completeness in Pennsylvania

Routine childhood administration of varicella-containing vaccine has resulted in the number of varicella (chickenpox) cases in Pennsylvania falling from nearly 3,000 cases in 2007 to less than 400 cases in 2017. Prior to 2018, the completeness of varicella case investigation data documented in Pennsylvania's electronic disease surveillance system (PA-NEDSS) was not routinely monitored by Department of Health (DOH) staff. A pilot project was initiated in April 2018 to monitor and improve completeness of select varicella case investigation variables.

June 18, 2019

Forming Collaborations through the Data Quality Committee to Address Urgent Incidents

On November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.

June 18, 2019

Monitoring and Improving Syndromic Surveillance Data Quality

The public health problem identified by Alabama Department of Public Health Syndromic Surveillance (AlaSyS) was that the data reflected in the user application of ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) was underestimating occurrences of syndromic alerts preventing Alabama Department of Public Health (ADPH) from timely recognition of potential public health threats.

June 18, 2019

Advanced Visualization and Analysis of Data Quality for Syndromic Surveillance Systems

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 researchers. 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.

January 21, 2018

Data Quality Improvements in National Syndromic Surveillance Program (NSSP) Data

The National Syndromic Surveillance Program (NSSP) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. These data, captured in nearly real time, are intended to improve the nation's situational awareness and responsiveness to hazardous events and disease outbreaks.

January 21, 2018

Nonparametric Models for Identifying Gaps in Message Feeds

Timely and accurate syndromic surveillance depends on continuous data feeds from healthcare facilities. Typical outlier detection methodologies in syndromic surveillance compare predictions of counts for an interval to observed event counts, either to detect increases in volume associated with public health incidents or decreases in volume associated with compromised data transmission.

January 25, 2018

Improving Syndromic Data Quality through Implementation of Error Capture Module

Oregon Public Health Division (OPHD), in collaboration with The Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2011. ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data from hospitals across Oregon. While each hospital system sends HL7 2.5.1-formatted messages, each uses a uniquely configured interface to capture, extract, and send data. Consequently, ESSENCE receives messages that vary greatly in content and structure.

January 25, 2018

Investigating Other Syndrome in ESSENCE from a Data Quality Perspective

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts syndromic surveillance of Emergency Department (ED) visits through the Louisiana Early Event Detection System (LEEDS) and submits the collected data to ESSENCE. There are currently 86 syndromes defined in LEEDS including infectious disease, injury and environmental exposure syndromes, among others. LEEDS uses chief complaint, admit reason, and/or diagnosis fields to tag visits to relevant syndromes.

January 25, 2018

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NSSP Community of Practice

Email: syndromic@cste.org

 

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