One of the significant challenges that multi-user biosurveillance systems have is alarm management. Currently deployed syndromic surveillance systems [1â3] have a single user interface. However, different users have different objectives; the alarms that are important for one category of user are irrelevant to the objectives of another category of user. For example, a physician wants to identify disease on an individual-patient level, a county health authority is interested in identifying disease outbreak as early as possible within his local region, while an epidemiologist at the national level is interested in global situational awareness. The objective of a multi-agent decision support system is not only to recognize patterns of epidemiologically significant events but also to indicate their relevance to particular user groupsâ objectives. Thus, instead of simply providing alerts of anomaly detections, the system architecture needs to provide analyzed information supporting multiple usersâ decisions.
Surveillance Systems
Disease surveillance systems are currently used for the early detection of disease outbreak before diagnosis is confirmed in order to mobilize a rapid response . The fear of epidemics or bioterrorism resulted in the development of systems for the general population; however research efforts for sensitive population groups are missing. Sensitive groups could be considered patients suffering from chronic diseases (such as diabetes and renal failure), elderly people and infants. It is well known that these groups are quite susceptible to diseases that can be easily spread under certain circumstances e.g. in a dialysis room where patients with renal failure receive their regular treatment. In addition to that, several diseases seem to affect them more. Therefore, the development of disease surveillance systems for sensitive population groups is an issue that should be addressed.
Objective
The aim of this study is to reveal the need for developing disease surveillance systems for sensitive populations.
Following an Oct 12-13, 2006 snowstorm, almost 400,000 homes in western New York lost power, some for up to 12 days. News reports said that emergency rooms saw many patients with CO exposure; 3 deaths were attributed to CO poisoning. As part of NYS DOH’s syndromic surveillance system, electronic ED records with a free-text CC field listing the symptoms reported by the patient are sent to NYS DOH daily. Each CC is searched for text strings indicating complaints in one or more of 6 syndromes (asthma, fever, gastrointestinal (GI), neurological, respiratory, rash). The system also allows nonroutine searches of CCs for complaints of interest. NYS hospitals also submit ED records to the Statewide Planning and Research Cooperative System (SPARCS) that include diagnostic codes assigned after evaluation of the patient (due within 30 days of each calendar quarter).
Objective
To assess the ability to identify cases of carbon monoxide (CO) poisoning from chief complaints (CC) in hospital emergency department (ED) records submitted daily to the New York State (NYS) Department of Health (DOH) Electronic Syndromic Surveillance System.
In 2004, the BioDefend (BD) syndromic surveillance (SS) system was implemented in Duval County hospitals (Jacksonville, FL). Daily emergency department chief complaints are manually classified and entered into the BD system by triage personnel. As part of a statewide implementation, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) began collecting data in the Jacksonville area during the winter of 2007-08. ESSENCE uses an automated data collection, chief complaint parsing and analysis process for data management and analysis. The use of two systems during the same period of time in one area provided a unique opportunity to retrospectively analyze characteristics of the BD and ESSENCE systems.
Objective
To compare detection of a community outbreak of influenza-like illness using two SS systems, one using a clinician’s classification of reason for visit and the other using an automated chief complaint parsing algorithm.
Emerging infections, both natural and intentional, have provided an impetus for improved disease surveillance and response. The recognition of the interdependence of health care systems and public health infrastructure provides an opportunity to expand beyond traditional disease-based surveillance to a more comprehensive, integrated approach that leverages existing electronic information. The Veterans Affairs (VA) hospital system is uniquely positioned to perform multi-institutional enhanced electronic surveillance. A wealth of electronic information and technology resources are available in all VA hospitals and their associated clinics, as each facility uses the same standardized Computer Patient Record System. Influenza-like illness (ILI) is a common clinical syndrome of diverse etiology that presents with respiratory and systemic symptoms. The NC health department mandates the reporting of ILI from emergency departments to facilitate monitoring of seasonal ILI and serve as an important component of pandemic preparedness. Existing surveillance systems utilize an ICD-9 respiratory code screen and subsequent manual chart review which is timeconsuming and insensitive. Automated medical record review using more comprehensive electronic data may improve the system’s timeliness and efficiency.
Objective
To use data collected by NC-VET to create an automated ILI surveillance program and compare its accuracy and efficiency to the existing program.
Many syndromic surveillance systems have been developed and are operational, yet lack concise guidelines for investigating and conducting followups on daily alarms. Daily emergency department visits from six reporting hospitals in the Duval County area are assessed and classified into a BioDefend (BD) system entry by triage personnel. Alarms are categorized into alerts, 3 SD above a 30 day rolling mean, or warnings, 2-3 SD above the mean. Signals are monitored and in response, public health investigations and recommended interventions are initiated.
Objective
To evaluate the protocol that the Duval County Health Department (DCHD) epidemiology staff uses to respond to BD syndromic surveillance system alarms. The response protocol utilizes all signals detected by BD and its secondary resources, within the DCHD jurisdiction.
Presented December 6, 2016
A comprehensive definition of a syndrome is composed of direct (911 calls, emergency departments, primary care providers, sensor, veterinary, agricultural and animal data) and indirect evidence (data from schools, drug stores, weather etc.). Syndromic surveillance will benefit from quickly integrating such data. There are three critical areas to address to build an effective syndromic surveillance system that is dynamic, organic and alert, capable of continuous growth, adaptability and vigilance: (1) timely collection of high quality data (2) timely integration and analysis of information (data in context) (3) applying innovative thinking and deriving deep insights from information analysis. In our view there is excessive emphasis on algorithms and applications to work on the collected data and insufficient emphasis on solving the integration challenges. Therefore, this paper is focused on information integration.
Objective
EII is the virtual consolidation of data from multiple systems into a unified, consistent and accurate representation. An analyst working in an EII environment can simultaneously view and analyze data from multiple data sources as if it were coming from one large local data warehouse. This paper posits that EII is a viable solution to implement a system covering large areas and disparate data sources for syndromic surveillance and discusses case studies from environments external to health.
The NNDSS is the public health surveillance system that enables all levels of public health (local, state, territorial, and federal) to monitor the occurrence and spread of the diseases and conditions that the Council of State and Territorial Epidemiologists (CSTE) has officially designated as being "nationally notifiable". The NNDSS data are a critical source of data for monitoring disease trends, effectiveness of prevention and control programs, and policy development. To provide timely NNDSS data, state and territorial health departments voluntarily report notifiable disease incidence data to CDC when they become aware of these cases and as per recommended national notification timeframes. These provisional data are published each week in Morbidity and Mortality Weekly Report (MMWR). Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data (1). However, there have been fewer efforts in the area of on-line dissemination of surveillance data, which is so important in maximizing the utility of collected data.
Objective
The purpose of this project was to identify ideas and potential options for an enhanced dissemination of provisional data for the US National Notifiable Diseases Surveillance System (NNDSS).
Los Alamos National Laboratory (LANL) has been funded by the Defense Threat Reduction Agency to develop tools that enhance situational awareness in infectious disease surveillance. We have applied the concept of the surveillance window to the development of a cross platform app (SWAP). This app allows the user to place information on case counts or disease occurrence in a specific location within the context of a historical outbreak curve to help determine whether prevention or mitigation action should be taken. By placing a frame of reference for where a case count is during an outbreak (in the early, peak, or late stages) and indicating whether the unfolding events are still within a surveillance window that would allow for feasible control, the app provides enhanced situational awareness of a decision maker. This tool therefore increases the granularity of situational awareness available to any user in the global biosurveillance community.
Objective
The goal of this project is to develop a cross platform app that contextualizes incoming information during an infectious disease outbreak based on historical data. The app makes use of a surveillance window concept in order to support decision making. This effort is part of a larger project with the goal of developing reference tools and analytics to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.