BioSense

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts emergency department (ED) syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). IDEpi has the capability to define and change syndrome definitions in LEEDS based on surveillance needs and quality assurance activities. IDEpi submits all of the ED data to BioSense, which uses different syndrome definitions than LEEDS. Both BioSense and LEEDS use text and ICD code searches in any available chief complaint, admit reason and diagnosis data. The results of LEEDS and BioSense syndrome classifications for influenza-likeillness (ILI), gastrointestinal (GI), and upper respiratory infections (URI) applied to Louisiana’s ED data were compared to examine if the different syndrome definitions yield similar results when applied to the same data.

Objective

To compare the results of BioSense and Louisiana syndrome classifications for influenza-like-illness, gastrointestinal, and upper respiratory infections applied to Louisiana emergency department data.

Per a frequently asked questions document on the ISDS website, approximately two thirds of HL7 records received in BioSense do not provide a Visit ID. As a result, BioSense data processing rules use the patient ID, facility ID and earliest date in the record to identify a unique visit. If the earliest dates in records with the same patient ID and facility ID occur within the same 24-hour time frame, those two visits are combined into one visit and the earliest date will be stored. The ED data sent by hospitals to NC DETECT include unique visit IDs and these are used to identify unique visits in NC DETECT. These data are also sent twice daily to BioSense. In order to assess the potential differences between the NC DETECT ED data in NC DETECT and the NC DETECT ED data in BioSense, an initial analysis of the 24-hour rule was performed.

Objective

NC DETECT emergency department (ED) data were analyzed to assess the impact of applying the BioSense “24-hour rule” that combines ED visits into a single visit if the patient ID and facility ID are the same and the earliest recorded dates occur within the same 24-hour time frame.

BioSense 2.0 uses predetermined syndromes based upon ICD-9 codes and chief complaint data to allow users to view and analyze data from emergency department (ED) visits, yet further validations of these syndromes are needed. Previous studies have validated syndromic surveillance syndromes by comparing chief complaint data to discharge diagnosis; however, these efforts are not possible for jurisdictions in which facilities do not submit ICD-9 code data. Currently in Utah, the syndromic surveillance data submitted includes only chief complaint information. Thus, efforts to validate BioSense syndromes, such the “poisoning by medicines” syndrome, can be informed by but not analyzed in accordance with ICD-9 code and discharge diagnosis data in Utah.

Objective

To evaluate the BioSense 2.0 “poisoning by medicines” syndrome by determining chief complaint terms for inclusion and exclusion based upon pre-defined ICD-9 codes and a comparison of binned and unbinned chief complaint data.

The Public Health Security and Bioterrorism Preparedness and Response Act of 2002 mandated establishing an integrated national public health surveillance system for early detection and rapid assessment of potential bioterrorism-related illness. In 2003, CDC created and launched the BioSense software program. At that time, CDC’s focus was on rapidly developing and implementing Web-based software to collect hospital emergency department data for analysis to detect and monitor syndromes of public health importance. During the ensuing decade, BioSense evolved and now is part of CDC’s renamed National Syndromic Surveillance Program (NSSP). The broader vision of NSSP aims to achieve two key goals: significantly improve technical capabilities for collecting and analyzing syndromic surveillance data, and to create and facilitate opportunities for collaboration among local, state, and national public health programs. Through NSSP, the syndromic surveillance community can be strengthened by access to improved technical capacity and to best-practices knowledge sharing among syndromic surveillance professionals. These NSSP initiatives can help the nation-wide public health community strengthen situational awareness and enhance response capability to hazardous events. NSSP encompasses people, partners, policies, information systems, standards, and resources. Session attendees will learn more about NSSP, its growing group of partners, what the program is doing now, and its future.

Objective

Inform conference attendees about the CDC National Syndromic Surveillance Program (NSSP), various program-related projects and who is working on them, what was accomplished during the past year, and NSSP-development plans for the future.

One of the greatest hurdles for BioSense Onboarding is the process of validating data received to ensure it contains Data Elements of Interest (DEOI) needed for syndromic surveillance. Efforts to automate this process are critical to meet existing and future demands for facility onboarding requests as well as provide a foundation for data quality assurance efforts. By automating the validation process, BioSense hopes to:

1. Reduce costs associated with the iterative validation process.

2. Improve BioSense response times for assistance with onboarding.

3. Improve documentation to partners about requirements and communicate changes to DEOI.

4. Provide a better foundation for data quality initiatives.

Efforts to improve data validation are being developed in alignment with BioSense future initiatives and will apply to both BioSense, Essence and other BioSense program applications.

BioSense Onboarding identified critical success factors by participating in ISDS workgroup initiatives for Onboarding and Data Quality and soliciting feedback from key jurisdictional partners. These critical success factors include; improved documentation, access to raw data, and faster validation response time.

Objective

This session will inform the BioSense Community about data validation advancements implemented this past year as well as future plans to improve the BioSense validation process to achieve emergency department representativeness goals.

Syndromic surveillance refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. Tennessee obtains emergency department data for syndromic surveillance in standardized HL7 format following the field and value set standards published by the Public Health Information Network. Messages contain information previously unavailable to syndromic surveillance systems, including quantitative values such as recorded temperature. Data are received daily and processed by a Tennessee ESSENCE application and the national BioSense platform.

These systems use chief complaint keywords, ICD9 codes, and other algorithms to assign syndromes for each record. The differences between the BioSense and ESSENCE syndrome assignments have not been well defined. Detailed comparisons of syndrome assignment across tools are difficult to perform due to the intensity of the manual review required. However, definitions of fever can be easily confirmed in HL7 messages when the recorded temperature is provided. Currently, both the BioSense and ESSENCE syndrome definitions exclude recorded temperature from consideration when assigning syndromes.

To compare the performance of the fever syndromes used by BioSense and ESSENCE, recorded temperature data was used as the gold standard.

Objective

To objectively compare the BioSense and ESSENCE fever syndromes using recorded temperature as a gold standard.

Monitoring heat-related illness (HRI) is a public health priority in Maricopa County, Arizona. Since 2006, Maricopa County Department of Public Health has utilized data from hospital discharges, medical examiner preliminary reports, and death certificates to quantify heat-related morbidity and mortality, but these surveillance methods take time. Identifying HRI more quickly would improve situational awareness and allow public health officials to launch a more immediate response to extreme heat events. Arizona began using BioSense 2.0 in July 2014 to collect chief complaint and diagnosis data for syndromic surveillance. The BioSense Front End Application uses a standard query definition for HRI (i.e., “Heat, excessive”), but this definition may perform differently for each jurisdiction.

Objective

To evaluate the pre-defined “Heat, excessive” query in BioSense 2.0 using recent Maricopa County, Arizona data; quantify the number of cases retrieved by the query due to chief complaint terms rather than clinical diagnosis; and provide a list of terms to be considered for exclusion criteria while developing a custom query

BioSense was launched in 2003 by CDC with its primary aim to establish an integrated system of nationwide public health surveillance for the early detection and prompt assessment of potential bioterrorism-related syndromes or other public health emergencies. With the release of CDC’s Surveillance Strategy, BioSense evolved into the National Syndromic Surveillance Program (NSSP). To overcome the challenges experienced throughout the integration of local and state level data to produce a real-time national all-hazards surveillance, CDC sought input from the National Syndromic Surveillance Community of Practice (NSSP CoP). They requested that CDC provide advanced syndromic surveillance functionalities and analytical applications, such as ESSENCE and SAS to improve the BioSense Platform. In response, CDC led this pilot project to: 1) conduct security testing of SAS and ESSENCE in order to identify vulnerabilities; 2) test and improve a limited set of processes that occur before data are transformed; and 3) conduct testing of ESSENCE’s functions to ensure the tool worked as intended, and that it will meet user needs.

Objective

To describe the results of a pilot project that examined selected BioSense 2.0 data processing rules and tested SAS and ESSENCE products in the BioSense platform.