Data Flow and Data Quality - NSSP New Site Onboarding Window (Fall 2020) Webinar #2

The National Syndromic Surveillance Program (NSSP) Team hosted the 2nd webinar of its Fall 2020 New Site Onboarding Window on October 27, 2020. The webinar focuses on data ingestion into the NSSP BioSense Platform and data quality checks and reports.

View the recording of the webinar here.

February 10, 2021

Improving the Quality of Completeness and Electronic Health Record Data Used in Syndromic Surveillance Final Report

The Council of State and Territorial Epidemiology (CSTE), in collaboration with Thought Bridge, LLC, recently developed the Improving the Quality of Completeness and Electronic Health Record Data Used in Syndromic Surveillance Final Report which aimed to identify data quality issues and develop short- (6 months or less) and long-term (>6 months) recommendations. 

October 08, 2020

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

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

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

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

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