One limitation of syndromic surveillance systems based on emergency department (ED) data is the time and expense to investigate peak signals, especially when that involves phone calls or visits to the hospital. Many EDs use electronic medical records (EMRs) which are available remotely in real time. This may facilitate the investigation of peak signals.
This paper outlines the integration of hospital admission, Febrile Respiratory Illness (FRI) screening and Canadian Triage and Acuity Score (CTAS) data streams within an Emergency Department Syndromic Surveillance system. These data elements allow better characterization of outbreak severity and enable more effective resource allocation within acute care settings.
This poster describes the practical integration of Early Event Detection (EED) into the daily operation of a medium sized public health department to improve surveillance for, and response to, outbreaks of communicable disease.
Current state-of-the-art outbreak detection methods [1-3] combine spatial, temporal, and other covariate information from multiple data streams to detect emerging clusters of disease. However, these approaches use fixed methods and models for analysis, and cannot improve their performance over time. Here we consider two methods for overcoming this limitation, learning a prior over outbreak regions and learning outbreak models from user feedback, using the recently proposed multivariate Bayesian scan statistic (MBSS) framework [1]. Given a set of outbreak types {Ok}, set of space-time regions S, and the multivariate dataset D, MBSS computes the posterior probability Pr(H1(S, Ok) | D) of each outbreak type in each region, using Bayes’ Theorem to combine the prior probabilities Pr(H1(S, Ok)) and the data likelihoods Pr(D | H1(S, Ok)). Each outbreak type can have a different prior distribution over regions, as well as a different model for its effects on the multiple streams. The set of outbreak types, as well as the region priors and outbreak models for each type, can be learned incrementally from labeled data or user feedback.
Objective
We argue that the incorporation of machine learning algorithms is a natural next step in the evolution and improvement of disease surveillance systems. We consider how learning can be incorporated into one recently proposed multivariate detection method, and demonstrate that learning can enable systems to substantially improve detection performance over time.
In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, SNA was demonstrated to be a good supplementary tool for contact tracing [1]. Traditionally, social networks for disease investigation are constructed only with personal contacts since personal contacts are the most identifiable paths for disease transmission. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients [2][3].
Objective
In this research, we aim to investigate the necessity of incorporating geographical contacts into Social Network Analysis (SNA) for contact tracing in epidemiology and explore the strengths of multi-mode networks with patients and geographical locations in network visualization for disease spread investigation.
Syndromic surveillance of emergency department(ED) visit data is often based on computerized classifiers which assign patient chief complaints (CC) tosyndromes. These classifiers may need to be updatedperiodically to account for changes over time in the way the CC is recorded or because of the addition of new data sources. Little information is available as to whether more frequent updates would actually improve classifier performance significantly. It can be burdensome to update classifiers which are developed and maintained manually. We had available to us an automated method for creating classifiers thatallowed us to address this question more easily. The âNgramâ method, described previously, creates a CC classifier automatically based on a training set of patient visits for which both the CC and ICD9 are available. This method measures the associations of text fragments within the CC (e.g. 3 characters for a â3-gramâ) with a syndromic group of ICD9 codes. It then automatically creates a new CC classifier based on these associations. The CC classifier thus created can then be deployed for daily syndromic surveillance.
Objective
Our objective was to determine if performance of the Ngram classifier for the GI syndrome was improved significantly by updating the classifier more frequently.
Case detection from chief complaints suffers from low to moderate sensitivity. Emergency Department (ED) reports contain detailed clinical information that could improve case detection ability and enhance outbreak characterization. We developed a text processing system called Topaz that could be used to answer questions from ED reports, such as: How many new patients have come to the ED with acute lower respiratory symptoms? Of the respiratory patients, how many had a productive cough or wheezing? How many of the respiratory patients have a past history of asthma?
Objective
To evaluate how well a text processing system called Topaz can identify acute episodes of 55 clinical conditions described in ED notes.
Health care workers (HCWs) have an increased risk of exposure to infectious agents including (among others) tuberculosis, influenza, norovirus, and Clostridium difficile as a consequence of patient care1,2 Most occupational transmission is associated with violation of one or more basic principles of infection control: handwashing; vaccination of HCWs; and prompt isolation.3 OH surveillance is paramount in guiding efforts to improve worker safety and health and to monitor trends and progress over time.4 GIS can assist in supporting health situation analysis and surveillance for the prevention and control of health problems, for example: by creating temporal-spatial maps of outbreaks, public health workers can visualize the spread of cases as the outbreak progresses; spatial/database queries allow for selection of a specific location or condition to focus public health resources.
Objective
This paper describes a GIS tool which maps the floors and departments of a Southeastern Ontario tertiary care hospital for the purpose of monitoring respiratory and gastrointestinal (GI)-related Occupational Health (OH) visits among hospital employees.
Heat surveillance in Houston is currently limited to mortality reports from the medical examiners office. A possible source of heat related morbidity is the Houston Real-time Outbreak Disease Surveillance (RODS) system. The RODS system was put into practice in the Houston Department of Health and Human services (HDHHS) in 2004 and now encompasses 37 hospitals. While initially designed for early detection of bioterrorism events, using syndromic data to detect other medical complaints, such as heat related morbidity, could prove to be beneficial and cost-effective for large cities, such as Houston.
Objective
The purpose of this investigation is to determine the value of using the RODS system to track heat-related morbidity in Houston, Texas.
With increased penetration of clinical information system products and increased interest in clinical data exchange, a variety of clinician’s notes are becoming available for surveillance. Chief complaints have been studied extensively, and emergency department notes have received attention, but narrative clinic visit notes have gotten little attention.
Objective
To assess the performance of an unmodified, general purpose natural language processing system to detect fever, and to assess the feasibility of parsing visit notes for syndromic surveillance.