English Roseanne

The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.

BioSense is a CDC initiative to promote situational awareness through summarizing, analyzing, and presenting health related event information. Among the data sources collected and analyzed through the BioSense application are the Department of Defense and Department of Veterans Affairs ambulatory clinic care data. Clinical diagnoses and procedures are quantified, and analytic results are presented and categorized into 94 state and metropolitan areas.

Objective

Precise geographic location of health events is a challenging but critical component to determine the likely site of exposure for disease surveillance. This paper describes a method used by BioSense to develop and implement a reasonable set of rules in defining geographic locations of health events.

Objective The objective of this study was to determine which chief complaints and ICD-9-CM coded diagnoses from real-time BioSense hospital data correlate well with data from conventional influenza surveillance systems.

To determine the feasibility of using BioSense laboratory data to do surveillance on Clostridium difficile infection (CDI) and calculate overall and facility rates of disease.

Analysis of the BioSense data facilitates the identification, tracking, and management of emergent and routine health events, including potential bioterrorism events, injury related incidents and rapidly spreading naturally occurring events (1).  BioSense enhances coordination between all levels of public health and healthcare by providing access to the same data at the same time which can ultimately produce a faster and more coordinated response.  BioSense is a network of networks rather than a stand-alone program. Analysts at the BioIntelligence center (BIC) analyze and track BioSense data activity at a national level and support state and local public health system users (2).

Objective:

BioSense is a national human health surveillance system designed to improve the nationís capabilities for disease detection, monitoring, and real-time health situational awareness.

BioSense is a national program designed to improve the nation’s capabilities for conducting disease detection, monitoring, and real-time situational awareness. Currently, BioSense receives near real-time data from non-federal hospitals, as well as national daily batched data from the Departments of Defense and Veteran’s Affairs facilities.  These data are analyzed, visualized, and made simultaneously available to public health at local, state, and federal levels through the BioSense application.

Objective:

In this paper we present summary information on the non-federal hospitals currently sending data to the BioSense system and describe this distribution by hospital type, method of data delivery as well as patient class and patient health indicator.

Influenza surveillance provides public health officials and healthcare providers with data on the onset, duration, geographic location, and level of influenza activity in order to guide the local use of interventions. The Influenza Sentinel Provider Surveillance Network tracks influenza-like illness (% ILI) across the U.S. population. Objective: This presentation describes the use of influenza antiviral data from retail pharmacies to supplement influenza surveillance.

Since July 2004 the BioSense program at the Centers for Disease Control and Prevention (CDC) has received data from DoD military and VA outpatient clinics (not in real time). In January 2006 real-time hospital data (e.g. chief complaints and diagnoses) was added. Various diagnoses from all sources are binned into one or more of 11 syndrome categories.

Objective

This paper'­s objective is to compare syndromic categorization of newly acquired real-time civilian hospital data with existing BioSense data sources.

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. During CDC’s previous implementation of a syndromic surveillance system (BioSense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. The BioSense Governance Group Data Quality Workgroup paved the way to rethink surveillance data flow and quality. Their work and collaboration with state and local partners led to NSSP redesigning the program’s data flow. The new data flow provided a ripe opportunity for NSSP analysts to study the data landscape (e.g., capturing of HL7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. In addition, NSSP extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. Even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. However, these issues were now identified. The newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. Therefore, an important component of the NSSP data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings.

Objective:

Review the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance.

Between 2006 and 2013, the rate of emergency department (ED) visits related to mental and substance use disorders increased substantially. This increase was higher for mental disorders visits (55 percent for depression, anxiety or stress reactions and 52 percent for psychoses or bipolar disorders) than for substance use disorders (37 percent) visits. This increasing number of ED visits by patients with mental disorders indicates a growing burden on the health-care delivery system. New methods of surveillance are needed to identify and understand these changing trends in ED utilization and affected underlying populations. Syndromic surveillance can be leveraged to monitor mental health-related ED visits in near real-time. ED syndromic surveillance systems primarily rely on patient chief complaints (CC) to monitor and detect health events. Some studies suggest that the use of ED discharge diagnoses data (Dx), in addition to or instead of CC, may improve sensitivity and specificity of case identification.

Objective: The objectives of this study are to

(1) create a mental health syndrome definition for syndromic surveillance to monitor mental health-related ED visits in near real time;

(2) examine whether CC data alone can accurately detect mental health related ED visits; and

(3) assess the added value of using Dx data to detect mental health-related ED visits.