The purpose of this paper is to describe the use of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) and its ability to use hospital emergency room data for situational awareness.
The existing New York State Department of Health emergency department syndromic surveillance system has used patientâs chief complaint (CC) for assigning to six syndrome categories (Respiratory, Fever, Gastrointestinal, Neurological, Rash, Asthma). The sensitivity and specificity of the CC computer algorithms that assign CC to syndrome categories are determined by using chart review as the criterion standard. These analyses are used to refine the algorithm and to evaluate the effect of changes in the syndrome definitions. However, the chart review (CR) method is labor intensive and expensive. Using an automated ICD9 code-based assignment as a surrogate for chart review could offer a significant cost reduction in this process and allow us to survey a much larger sample of visits.
Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender.
The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically process large amounts of information in order to rapidly provide the user with a situational awareness. Most systems currently deployed in health departments use only statistical algorithms to filter data for decision-making. These algorithms are capable of high sensitivity, but this sensitivity comes at the cost of excessive false positives [2], especially when multiple syndrome groups and data types are processed.
Objective
An intelligent information fusion approach is proposed to identify and provide early alerting of naturally-occurring disease outbreaks, as well as bioterrorist attacks, while reducing false positives. The proposed system statistically preprocesses information from multiple sources and fuses it in a manner comparable with the domain expert's decision-making process. Currently, system users lower the false alarm rate by "explaining away" the statistical data anomalies with alternative hypotheses derived from external, non-syndromic knowledge. We seek to incorporate this heuristic decision-making into a probabilistic network that accepts the outputs of statistical algorithms in a hybrid model of domain knowledge and data inference.
Objective: Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Our objective was to develop an anomaly detection algorithm that adapts to the time series being analyzed and reduces false positive signals. Background: Earlier we have presented studies with HWR, where the alerts were generated using a logical OR of several different criteria [1]. The anomaly detection contest required a continuous score for each day of the time series. This gave the impetus to develop a new version of our algorithm.
We sought to evaluate the validity of pneumonia and influenza hospitalizations (PI) data gathered by our biosurveillance system.
Space-time detection of disease clusters can be a computationally intensive task which defies the real time constraint for disease surveillance. At the same time, it has been shown that using exact patient locations, instead of their representative administrative regions, result in higher detection rates and accuracy while improving upon detection timeliness. Using such higher spatial resolution data, however, further exacerbates the computational burden on real time surveillance. The critical need for real time processing and interpretation of data dictate highly responsive models that may be best achievable utilizing high performance computing platforms.
Objective
Space-time detection techniques often require computationally intense searching in both the time and space domains. We introduce a high performance computing technique for parallelizing a variation of space-time permutation scan statistic applied to real data of varying spatial resolutions and demonstrate the efficiency of the technique by comparing the parallelized performance under different spatial resolutions with that of serial computation.
Determine if poor air quality resulting from wildfires could be measured in the general population by monitoring smoke-related respiratory Emergency Department (EDs) visits.
Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.
GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system. GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department (ED). The goal of this system is to assist clinicians in detecting potential BTAs as quickly and effectively as possible in order to better respond to and mitigate the effects of a large-scale outbreak.
GUARDIAN improves the diagnostic process by moving away from simple trend anomaly detection and towards the development of a BTA-specific infectious disease expert system [1]. Through the capture and automated application of specific clinical expertise, GUARDIAN provides the focus and accuracy necessary for effective BTA infection diagnosis. The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.
Although norovirus (NoV) is the most common cause of acute gastroenteritis (ewinter vomiting diseaseÃ), its contribution to mortality remains unknown and may be an unrecognized problem [1]. In Europe a genetic shift in circulating NoV strains was observed in 2002 which coincided with an unusually high number of NoV outbreaks in all but one country participating in the European NoV-surveillance network [2]. Covering a time period which included this outbreak peak, we used general practitioner (GP), hospital, and death-cause data in combination with NoV surveillance data to explore the association between NoV outbreaks and morbidity and mortality.