Emergency Department (ED)

Achieving health equality is a national priority. The surveillance of health disparities in minority populations is key for the advancement of health equality. However, the need for improvement in documentation of race and ethnicity has been identified across various public health data sets. Currently, due to the lack of reporting of race and ethnicity in HDD, the NDHHS mainly depends on analyses of the statewide Behavioral Risk Factor Surveillance System and Vital Records data for the surveillance of health disparities among minority populations. An alternative data set that might help inform the surveillance of health disparities is SyS data. This near-real–time electronic health record data is characterized by required core data elements that provide information about the date and time of patient encounter, treating facility, clinical information, and patient demographics. Previously, we demonstrated statistically significant correlations between the 2011 and 2012 NDHHS ED SyS and ED HDD data for ICD9-CM ECODES corresponding to motor vehicle crash related injury, which is a relevant cause of health disparities. Our new objective was to determine the reporting consistency of ICD9-CM ECODES associated with other injury related health disparities between 2013 NDHHS SyS and HDD ED data. We also sought to determine if near-real–time ED and IP SyS data provide a more complete documentation of race and ethnicity than HDD.

Objective

This pilot study evaluates Nebraska Department of Health and Human Services (NDHHS) emergency department (ED) syndromic surveillance (SyS) data quality by cross-validating reported external cause of injury codes (ECODES) associated to racial/ethnic injury health disparities in Nebraska. The percent completeness of core data elements in SyS data and hospital discharge data (HDD) was also determined.

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).

During the past ten years, the syndromic surveillance has mainly developed thanks to clinical data sources (i.e. emergency department, emergency medical call system, etc.). However, in these systems, the population doesn’t play an active role. It is now important that the population becomes an actor of this surveillance; especially since several European experiences about influenza showed that the population could participate to an internet-based monitoring. In Reunion Island, the population is very sensitive to public health concerns. In this context, the health authorities implemented since April 2014 a web-based surveillance system, called “Koman i lé”, that allows to follow the perceived health among people who don’t systematically see their general practitioner.

Objective

To describe a new surveillance system based on an online selfreported symptoms and to present the first results.

Per a frequently asked questions document on the ISDS website, approximately two thirds of HL7 records received in BioSense do not provide a Visit ID. As a result, BioSense data processing rules use the patient ID, facility ID and earliest date in the record to identify a unique visit. If the earliest dates in records with the same patient ID and facility ID occur within the same 24-hour time frame, those two visits are combined into one visit and the earliest date will be stored. The ED data sent by hospitals to NC DETECT include unique visit IDs and these are used to identify unique visits in NC DETECT. These data are also sent twice daily to BioSense. In order to assess the potential differences between the NC DETECT ED data in NC DETECT and the NC DETECT ED data in BioSense, an initial analysis of the 24-hour rule was performed.

Objective

NC DETECT emergency department (ED) data were analyzed to assess the impact of applying the BioSense “24-hour rule” that combines ED visits into a single visit if the patient ID and facility ID are the same and the earliest recorded dates occur within the same 24-hour time frame.

SCRAs are accessible and affordable, sold online, in gas stations, and in “head” shops for $5-30 per package.[1] While marijuana is a schedule 1 narcotic, unavailable for any use, SCRAs navigate the legal landscape with marketing as non-consumable and frequent modifications to the active ingredients that outpace lawmakers’ updates. When consumed, SCRAs bind the same receptor as the active ingredient in marijuana with 10-1000 times the affinity. Physical reactions to marijuana use include breathing problems, increased heart rate, hallucinations, paranoia, lower blood pressure, and dizziness. [2] Health departments have reported varying clinical presentations in response to SCRAs, including extreme agitation and tachycardia. Ongoing reports of SCRA reactions and rising marijuana legalization emphasize the imperative to leverage syndromic surveillance to monitor trends, detect emerging outbreaks, and observe changes in clinical presentations or user demographics.

Objective

Use syndromic surveillance to identify and monitor adverse health events resulting from synthetic cannabinoid receptor agonists (SCRAs) or marijuana. Characterize the current trend of SCRAs and marijuana use among emergency department (ED) and urgent care center (UCC) visits in Virginia to determine whether findings align with utilization trends identified by other states from poison control center calls and ED visits.

Syndromic surveillance has historically been used to track infectious disease, but in recent years, many jurisdictions have utilized the systems to conduct all hazards surveillance and provide situational awareness with respect to previously identified issues. Flakka is a synthetic drug (class: cathinones) that recently has been featured in the media. Flakka is a stimulant that causes delusions, aggression, erratic behavior, a racing heart and sometimes death. Two specific counties (one in Florida and one in Kentucky) have been at the center of this emerging epidemic. In August 2015, Florida Department of Health (FDOH) partner agencies requested flakka-related health data in an effort to better understand the epidemiology and context of this problem. ESSENCE-FL is a large syndromic surveillance system, with four main data sources, that captures 87% of all emergency department (ED) visits statewide.

Objective

To characterize flakka usage in Florida using multiple data sources within the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL)

Healthcare seeking behavior is important to understand when interpreting public health surveillance data, planning for healthcare utilization, or attempting to estimate or model consequences of an adverse event, such as widespread water contamination. Although there is evidence that factors such as perceived susceptibility and benefits affect healthcare seeking behavior, it is difficult to develop accurate assumptions due to a lack of published research on this topic. Current conceptual behavior models, such as the health belief model, are not easily translated into quantifiable terms.

Objective

This paper describes analyses of health seeking behaviors from two surveillance datastreams: Poison Control Center (PCC) calls and Emergency Department (ED) visit records. These analyses were conducted in order to quantify behaviors following the development of symptoms after water contamination exposure and to understand the motivation, decision-making and timing behind healthcare seeking behaviors.

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).

The advent of Meaningful Use (MU) has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. The triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. Triage notes are readily available in many ED information systems, including, but not limited to, Allscripts, Cerner, EPIC, HMS, MedHost, Meditech, and T-System. North Carolina’s syndromic surveillance system, NC DETECT, currently collects triage notes from 33 out of 122 hospitals in the State (27%), and this number is likely to increase.

Objective

This roundtable will provide a forum for the ISDS community to discuss the use of emergency department (ED) triage notes in syndromic surveillance. It will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field.