BioSense

Lack of access to regular dental care often results in costly, oral health visits to EDs that could otherwise have been prevented or managed by a dentist (1). Most studies on oral health-related visits to EDs have used a wide range of classifications from different databases, but none have used syndromic surveillance data. The volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for NC EDs.

Objective:

To develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in North Carolina (NC) Emergency Departments (EDs) using syndromic surveillance system data.

 

Understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance. In 2004, an estimated one fourth of the US adults reported having a mental illness in the previous year (1). Studies showed that mental illness exacerbates multiple chronic diseases like cardiovascular diseases, diabetes and asthma (2). BioSense is a national electronic public health surveillance system developed by the Centers for Disease Control and Prevention (CDC) that receives, analyzes and visualizes electronic health data from civilian hospital emergency departments (EDs), outpatient and inpatient facilities, Veteran Administration (VA) and Department of Defense (DoD) healthcare facilities. Although the system is designed for early detection and rapid assessment of all-hazards health events, BioSense can also be used to examine patterns of healthcare utilization.

Objective:

The purpose of this paper was to analyze the associated burden of mental illness and medical comorbidity using BioSense data 20082011.

Monitoring laboratory test reports could aid disease surveillance by adding diagnostic specificity to early warning signals and thus improving the efficiency of public health investigation of detected signals. Laboratory data could also be employed to direct and evaluate interventions and countermeasures, while monitoring outbreak trends and progress; this would ultimately result in better outbreak response and management, and enhanced situation awareness. Since Electronic Laboratory Reporting (ELR) has the potential to be more accurate, timely, and cost-effective than reporting by other means of communication (e.g., mail, fax, etc.), ELR adoption has been systematically promoted as a public health priority.  However, the continuing use of non-standard, local codes or text to represent laboratory test type and results complicates the use of ELR data in public health practice. Use of structured, unique, and widely available coding system(s) to support the concepts represented by locally assigned laboratory test order and result information improves the computational characteristics of ELR data. Out of several coding strategies available, the Office of the U.S. National Coordinator for Health Information Technology has recently suggested incorporating Logical Observation Identifiers Names and Codes (LOINC) for laboratory orders and Systemized Nomenclature of Medicine- Clinical Terms (SNOMED CT) codes for laboratory results to standardize ELR.

Objective:

To examine the use of LOINC and SNOMED CT codes for coding laboratory orders and results in laboratory reports sent from 63 non-federal hospitals to the BioSense Program in calendar year 2011.

 

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. In 2010, the BioSense Program began a redesign effort to improve features such as centralized data mining and addressing concerns that the system could not meet its original objective to provide early warning or detect local outbreaks.

Objective

To familiarize public health practitioners with the BioSense 2.0 application and its use in all hazard surveillance.

 

BioSense 2.0, a redesigned national syndromic surveillance system, provides users with timely regional and national data classified into disease syndromes, with views of health outcomes and trends for use in situational awareness. As of July 2014, there are 60 jurisdictions nationwide feeding data into BioSense 2.0. In New Jersey, the state’s syndromic surveillance system, EpiCenter, receives registration data from 75 of NJ’s 80 acute care and satellite emergency departments. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. To participate in BioSense 2.0, New Jersey worked with HMS to connect existing data to BioSense. In May, 2013, HMS established a single data feed of New Jersey’s facility data to BioSense 2.0. This transfer from HMS servers occurs twice daily via SFTP. The average daily visit volume in the transfer is around 10,000 records. This data validation project was initiated by the New Jersey Department of Health (NJDOH) in 2013 to assure that the registration records are delivered successfully to BioSense 2.0.

Objective

To assess and validate New Jersey’s ED registration data feed from EpiCenter to BioSense 2.0.

Timely access to Emergency Department (ED) Chief Complaint (CC) data, before the definitive diagnosis is established, allows for early outbreak detection and prompt response by public health officials.BioSense 2.0 is a cloud-based application that securely collects, tracks, and shares ED data from participating hospitals around the country. Denver Health (DH) is one of several Colorado hospitals contributing ED Chief Complaint data to BioSense 2.0. In August 2013, ED clinicians reported an increase in patients presenting with excited delirium, possibly related to synthetic marijuana (SM). We used this event to test the use of CC field of ED data for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.

Objective

The aims of this presentation is to use ED chief complaint data, to test BioSense 2.0 for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.

The May arrival of two cases of Middle East Respiratory Syndrome (MERS) in the US offered CDC’s BioSense SyS Program an opportunity to give CDC’s Emergency Operations Center (EOC) and state-and-local jurisdictions an enhanced national picture of MERS surveillance. BioSense jurisdictions can directly query raw data stored in what is known as “the locker.” However, CDC cannot access these data and critical functions, like creating ad-hoc syndrome definitions within the application are currently not possible. These were obstacles to providing the EOC with MERS information. BioSense staff developed a plan to 1) rapidly generate query definitions regardless of the locally preferred SyS tool and, 2) generate aggregate reports to support the national MERS response.

Objective

Demonstrate that information from disparate syndromic surveillance (SyS) systems can be acquired and combined to contribute to national-level situational awareness of emergent threats.

The benefits of inter-jurisdictional data sharing have been touted as a hallmark of BioSense 2.0, a cloud-based computing platform for syndromic surveillance. A key feature of the BioSense 2.0 platform is the ability to share data across jurisdictions with a standardized interface. Jurisdictions can easily share their data with others by selecting data sharing partners from a list of participating jurisdictions. Technically the process is simple, however there are several other considerations (discussed herein) to be taken into account before and after deciding to share data with the larger BioSense community. This green paper is a continuation of several discussions stemming from a workshop hosted by the International Society of Disease Surveillance (ISDS) in collaboration with the Association of State and Territorial Health Officials (ASTHO), with the support of the U.S. Centers for Disease Control and Prevention (CDC). This initial workshop brought together epidemiologists from city, county and state public health departments primarily located in the US Health and Human Services Region 5. The workshop documented (Appendix 1) a variety of known benefits to data sharing, including:

• Cross-border case-finding

• Identifying patterns or trends (local, state, regional, federal)

• Emergency preparedness planning and partner notification

• Estimating an end to an event, based on declining trends in neighboring areas

• Mutual aid

• Ensuring national situational awareness for federal partners

• Hypothesis generation and testing

• Retrospective analysis to improve public health practice Members of this workshop composed an open letter to the BioSense Governance Group (Appendix 2) reporting on the top priorities and suggestions for functionality and documentation that would support data sharing among regional partners. Several members of the workshop coordinated a roundtable discussion at the ISDS 2013 annual conference (Appendix 3).

The annual ISDS conference attracts members across disciplines including practical epidemiologists, statisticians, researchers, informaticians and academic scholars. The objective of the roundtable was to open the conversation to the wider surveillance community and find potential solutions to the three primary barriers to data sharing originally identified by the workshop: legal/ethical concerns; unknown quality of the shared data; and the need for more granular (user role-based) sharing.

Objective

The purpose of this paper is to summarize the general and breakout group discussions facilitated by the roundtable members. This paper does not make any specific policy recommendations, however, we intend for the feedback captured in this document to lead to improvements in the BioSense 2.0 platform and application. The goal is to increase meaningful inter-jurisdictional data sharing by identifying existing barriers and user-generated solutions.