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Hallock Marilyn

Adoption of electronic medical records is on the rise, due to the Health Information Technology for Economic and Clinical Health Act and meaningful use incentives. Simultaneously, numerous HIE initiatives provide data sharing flexibility to streamline clinical care. Due to the consolidated data availability in centralized HIE models, conducting syndromic surveillance using locally developed systems, such as GUARDIAN, is becoming feasible. During the past year, Chicago has embarked on a city-wide HIE deployment campaign. Perhaps the most unique aspect of this endeavor is that the data warehouse for the HIE is intricately tied to the GUARDIAN syndromic surveillance system.

Objective

The objective is to describe the technical process, challenges, and lessons learned in scaling up from a local to regional syndromic surveillance system using the MetroChicago Health Information Exchange (HIE) and Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification (GUARDIAN) collaborative initiative.

Submitted by elamb on

The Centers for Disease Control and Prevention case definition of influenza-like illness (ILI) as fever with cough and/or sore throat casts a wide net resulting in lower sensitivity which can have major implications on public health surveillance and response.

 

Objective

This study investigates additional signs and symptoms to further enhance the ILI case definition for real-time surveillance of influenza.

Referenced File
Submitted by elamb on

Detection of biological threat agents (BTAs) is critical to the rapid initiation of treatment, infection control measures, and public health emergency response plans. Due to the rarity of BTAs, standard methodology for developing syndrome definitions and measuring their validity is lacking.

 

Objective

The objective of this study is to outline and demonstrate the robust methodology used by Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification surveillance system to generate and validate BTA profiles.

Referenced File
Submitted by elamb on

Early detection of rarely occurring but potentially harmful diseases such as bio-threat agents (e.g., anthrax), chemical agents (e.g., sarin), and naturally occurring diseases (e.g., meningitis) is critical for rapid initiation of treatment, infection control measures, and emergency response plans. To facilitate clinicians’ ability to detect these diseases, various syndrome definitions have been developed. Due to the rarity of these diseases, standard statistical methodologies for validating syndrome definitions are not applicable.

 

Objective

To develop and test a novel syndrome definition validation approach for rarely occurring diseases.

Submitted by teresa.hamby@d… on

Special event driven syndromic surveillance is often initiated by public health departments with limited time for development of an automated surveillance framework, which can result in heavy reliance on frontline care providers and potentially miss early signs of emerging trends. To address timelines and reliability issues, automated surveillance system are required.

Objective

To develop and implement a framework for special event surveillance using GUARDIAN, as well as document lessons learned postevent regarding design challenges and usability.



 

Submitted by Magou on

Weather events such as a heat wave or a cold snap can cause a change to the number of patients and types of symptoms seen at a healthcare facility. Understanding the impact of weather patterns on ILI surveillance may be useful for early detection and trend analysis. In addition, weather patterns limit our ability to extrapolate data collected in one region to a different region, which may not share the same weather or periodic trends. By modeling these sources of variation, we can factor out their effects and increase the sensitivity of our overall surveillance system.

Objective

To develop a statistical model to account for weather variation in influenza-like illness (ILI) surveillance.

Submitted by teresa.hamby@d… on

The 2014 Ebola outbreak in West Africa is one of the largest Ebola outbreaks in history. Early detection is critical for rapid initiation of treatment, infection control and emergency response plans. To facilitate clinicians’ ability to detect Ebola, various syndrome definitions have been developed.

Objective

To develop and validate an Ebola virus disease syndrome definition within the GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification) surveillance system.

Submitted by rmathes on

Mapping ILI surveillance data can be useful in identifying the direction and speed of an outbreak and for focusing control measures for an efficient public health response. The Centers for Disease Control and Prevention’s (CDC) ILINet currently displays weekly ILI geographic data at a national/regional/state level, but this visual data could also be useful at the local level.

Objective

To create a local geographic influenza-like illness (ILI) activity report.

Submitted by teresa.hamby@d… on

Effective real-time surveillance of infectious diseases must strike a balance between reliability and timeliness for early detection. Traditional syndromic surveillance utilizes limited sections of the EMR, such as chief complaints and/or diagnosis. However, other sections of the EMR may contain more pertinent information than what is captured in a brief chief complaint. These other EMR sections may provide relevant information earlier in the patient encounter than at the diagnosis or disposition stage, which can appear in the EMR up to 24 hours after the patient’s discharge. Comprehensive analysis may identify the most relevant section of EMRs for surveillance of all major infectious diseases, including ILI.

Objective

To investigate which section(s) of a patient’s electronic medical record (EMR) contains the most relevant information for timely detection of influenza-like illness (ILI) in the emergency department (ED).

Submitted by Magou on

In recent years, the threat of pandemic influenza has drawn extensive attention to the development and implementation of syndromic surveillance systems for early detection of ILI. Emergency department (ED) data are key components for syndromic surveillance systems. However, the lack of standardization for the content in chief complaint (CC) free-text fields may make it challenging to use these elements in syndromic surveillance systems. Furthermore, little is known regarding how ED data sources should be structured or combined to increase sensitivity without elevating false positives. In this study, we constructed two different models of ED data sources and evaluated the resulting ILI rates obtained in two different institutions.

Objective

To compare the influenza-like illness (ILI) rates in the emergency departments (ED) of a community hospital versus a large academic medical center (AMC).

Submitted by rmathes on
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