Emergency Department (ED)

The NJ syndromic surveillance system, EpiCenter, developed an algorithm to quantify HRI visits using chief complaint data. While heat advisories are released by the National Weather Service, an effective HRI algorithm could provide real-time health impact information that could be used to provide supplemental warnings to the public during a prolonged heat wave.

Objective:

The purpose of this evaluation is to characterize the relationship between a patient’s initial hospital emergency room chief complaint potentially related to a heat-related illness (HRI) with final primary and secondary ICD-9 diagnoses.

 

The final rules released by the Centers for Medicare and Medicaid Services specified the initial criteria for eligible hospitals to qualify for an incentive payment by demonstrating meaningful use of certified Electronic Health Record (EHR) technology. Syndromic surveillance reporting is one of three public health objectives that eligible hospitals can choose for stage 1. The PHIN messaging guide for syndromic surveillance was published for hospitals to construct emergency department data using Admit Discharge Transfer (ADT) messages, with the minimum dataset that is standard among hospitals and public health agencies. Currently New York hospitals are reporting emergency department (ED) visit data to the NY syndromic surveillance (SS) system. Patient chief complaint data are monitored for trends of illness at the community level in order to detect possible outbreaks and situational awareness.

Objective: 

To evaluate the readiness and timeliness of ED data submitted by hospitals following PHIN syndromic surveillance messaging guide and to evaluate the availability of minimum data elements. To validate the accuracy and completeness of data from ADT messages compared with data currently reported to the NY syndromic surveillance system.

 

During summer 2012, Washington State Department of Health (WA DOH) surveyed ILINet providers and found that more than half either utilize their electronic medical record system (EMRS) to gather and report weekly ILINet data, or intend to implement queries to do so in the future. There are a variety of EMRS being used state-wide, and providers that currently utilize these systems to report ILINet data apply a wide range of methods to query their data. There exists great interest in the evaluation of ambulatory care data within the context of Meaningful Use and little research is published in this area. WA DOH sought to evaluate electronic data from WA outpatient clinic networks in order to determine if a syndromic ILI definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Objective:

To determine if a syndromic influenza-like illness (ILI) definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Cold weather exposure-related injuries range from hypothermia to less severe conditions such as frost bite, trench foot, and chilblains, which are all preventable causes of mortality and morbidity. In recent years, NYC has successfully used syndromic surveillance of heat-related ED visits to inform emergency response during heat waves. Similar timely surveillance of cold-exposure related injuries could also inform public health protection measures during severe winter weather or cold season power outages. We conducted a retrospective analysis to compare hypothermia and cold-injury patient case characteristics, as well as temporal and meteorological correlates, between syndromic surveillance data and hospital discharge data.

Objective:

1) Develop cold exposure-related injury syndromic case definitions

2) use historical data to compare trends among cases identified in syndromic surveillance and cases identified in NY Statewide Planning and Research Cooperative System (SPARCS) hospital discharge data to evaluate representativeness and

3) develop regression models to examine relationships with cold weather conditions, and compare relationships across case definitions and data sources.

 

Maine has been conducting syndromic surveillance since 2007 using the Early Aberration Reporting System (EARS). An evaluation of the syndromic surveillance system was conducted to determine if system objectives are being met, assess the system’s usefulness, and identify areas for improvement. According to CDC’s Guidelines for Evaluating Public Health Surveillance Systems, a surveillance system is useful if it contributes to the timely prevention and control of adverse health events. Acceptability includes the willingness of participants to report surveillance data; participation or reporting rate; and completeness of data.

Objective:

To assess the usefulness and acceptability of Maine’s syndromic surveillance system among hospitals who currently participate.

 

Syndromic surveillance data has predominantly been used for surveillance of infectious disease and for broad symptom types that could be associated with bioterrorism. There has been a growing interest to expand the uses of syndromic data beyond infectious disease. Because many of these conditions are specific and can be swiftly diagnosed (as opposed to infectious agents that require a lab test for confirmation) there could be added value in using the ICD9 ED discharge diagnosis field collected by SS. However, SS discharge diagnosis data is not complete or as timely as chief complaint data. Therefore, for the time being SS chief complaint data is relied on for non-infectious disease surveillance. SPARCS data are based on clinical diagnoses and include information on final diagnosis, providing a means for comparing the chief complaint (from SS) to a diagnosis code (from SPARCS), for evaluating how well the syndrome is captured by SS and for assessing if it would be advantageous to get SS ED diagnosis codes in a more timely and complete manner.

Objective:

To evaluate several non-infectious disease related syndromes that are based on chief complaint (cc) emergency department (ED) syndromic surveillance (SS) data by comparing these with the New York Statewide Planning and Research Cooperative System (SPARCS) clinical diagnosis data. In particular, this work compares SS and SPARCS data for total ED visits and visits associated with three noninfectious disease syndromes, namely asthma, oral health and hypothermia.

 

North Carolina hosted the 2012 Democratic National Convention, September 3-6, 2012. The NC Epidemiology and Surveillance Team was created to facilitate enhanced surveillance for injuries and illnesses, early detection of outbreaks during the DNC, assist local public health with epidemiologic investigations and response, and produce daily surveillance reports for internal and external stakeholders. Surveillane data were collected from several data sources, including North Carolina Electronic Disease Surveillance System (NC EDSS), triage stations, and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). NC DETECT was created by the North Carolina Division of Public Health (NC DPH) in 2004 in collaboration with the Carolina Center for Health Informatics (CCHI) in the UNC Department of Emergency Medicine to address the need for early event detection and timely public health surveillance in North Carolina using a variety of secondary data sources. The data from emergency departments, the Carolinas Poison Center, the Pre-hospital Medical Information System (PreMIS) and selected Urgent Care Centers were available for monitoring by authorized users during the DNC.

Objective:

To describe how the existing state syndromic surveillance system (NC DETECT) was enhanced to facilitate surveillance conducted at the Democratic National Convention in Charlotte, North Carolina from August 31, 2012 to September 10, 2012.

 

Early detection of influenza outbreaks is critical to public health officials. Case detection is the foundation for outbreak detection. Previous study by Elkin el al. demonstrated that using individual emergency department (ED) reports can better detect influenza cases than using chief complaints. Our recent study using ED reports processed by Bayesian networks (using expert constructed network structure) showed high detection accuracy on detection of influenza cases.

Objective

Compare 7 machine learning algorithms with an expert constructed Bayesian network on detection of patients with influenza syndrome.