Monitoring and Improving Syndromic Surveillance Data Quality

The public health problem identified by Alabama Department of Public Health Syndromic Surveillance (AlaSyS) was that the data reflected in the user application of ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) was underestimating occurrences of syndromic alerts preventing Alabama Department of Public Health (ADPH) from timely recognition of potential public health threats.

June 18, 2019

UX Case Study: Tracking EHR automation, scarcity of attention, and transaction hazards

In recent years, studies in health and medicine have shifted toward eHealth communication and the relationships among human interaction, computer literacy, and digital text content in medical discourses (1-6). Clinicians, however, continue to struggle with EHR usability, including how to effectively capture patient data without error (7-9). Usability is especially problematic for clinicians, who must now acquire new skills in electronic documentation (10).

June 18, 2019

An Algorithm for Early Outbreak Detection in Multiple Data Streams

Current biosurveillance systems run multiple univariate statistical process control (SPC) charts to detect increases in multiple data streams. The method of using multiple univariate SPC charts is easy to implement and easy to interpret. By examining alarms from each control chart, it is easy to identify which data stream is causing the alarm. However, testing multiple data streams simultaneously can lead to multiple testing problems that inflate the combined false alarm probability.

June 18, 2019

Multidimensional Semantic Scan for Pre-Syndromic Disease Surveillance

An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined illness categories1.

June 18, 2019

What Can You Really Do with 35,000 Statistical Alerts a Week Anyways?

The National Syndromic Surveillance Program's (NSSP) instance of ESSENCE* in the BioSense Platform generates about 35,000 statistical alerts each week. Local ESSENCE instances can generate as many as 5,000 statistical alerts each week. While some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. By design, statistical alerts should indicate potential clusters that warrant a syndromic surveillance practitioner's time and focus.

June 18, 2019

Application Of Intelligent Multiagent Approach To Lyme Disease Simulation

Climate warming, globalization, social and economic crises lead to the activation of natural foci of vector-borne infections, among which a special place belongs to Lyme disease (Ixodic tick borreliosis – ITB), the vectors of which are the Ixodes ticks. More than 5,000 cases are registered in the United States every year. In European countries, the number of cases may reach up to 8,000-10,000 per year. Incidence rate for ITB in France is 39.4 per 100,000 population, in Bulgaria – 36.6.

June 18, 2019

New technologies to treatment of Spotted Fever, GVE VII - Santo Andre, SP, Brazil.

The use of new technologies such as Online Maps and the QR Code facilitates the knowledge dissemination in the health science, aiding in diagnostic elucidation and intelligent decisions making, thus offering an improvement in the quality of care provided to patients. Cases with suspected spotted fever should be approached as potentially serious, which may develop with shock within a few hours and, if not addressed can progress to death. In the case of spotted fever, early onset determines the cure of these cases.

June 18, 2019

Comparing spatio-temporal methods of non-communicable disease surveillance.

Health surveillance is well established for infectious diseases, but less so for non-communicable diseases. When spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. Our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. SatScan and Cusum) and Bayesian (incl. BaySTDetect and Dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease.

June 18, 2019

On estimation the relative risk of small area and visualization spatio-temporal map

Disease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden.

June 18, 2019

Cross Disciplinary Consultancy: Negation Detection Use Case

Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions.

June 18, 2019


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