Chief Complaint

Block 3 of the US Military Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE) system affords routine access to multiple sources of data. These include administrative clinical encounter records in the Comprehensive Ambulatory Patient Encounter Record (CAPER), records of filled prescription orders in the Pharmacy Data Transaction Service, developed at the Department of Defense (DoD) Pharmacoeconomic Center, Laboratory test orders and results in HL7 format, and others. CAPER records include a free-text Reason for Visit field, analogous to chief complaint text in civilian records, and entered by screening personnel rather than the treating healthcare provider. Other CAPER data fields are related to case severity. DoD ESSENCE treats the multiple, recently available data sources separately, requiring users to integrate algorithm results from the various evidence types themselves. This project used a Bayes Network approach to create an ESSENCE module for analytic integration, combining medical expertise with analysis of 4 years of data using documented outbreaks.

Objective

The project objective was to develop and test a decision support module using the multiple data sources available in the U.S. DoD version of ESSENCE.

A goal of biosurveillance is to identify incidents that require a public health response. The challenge is creating specific definitions of such incidents so they can be detected. In syndromic surveillance, this is accomplished by classifying emergency department chief complaints, nurse triage calls, and other prediagnostic data into categories, and then looking for increases in visits related to those categories. This approach can only find incidents that match those predefined categories. It is well-suited to handle common diseases; data from prior years provides information not only on which symptoms correlate with the disease, but also on how patients report them and how they appear in prediagnostic data streams. For unique or rare events, it is hard to know in advance how they will be described or recorded. Another approach is to look for similarities in the time of the healthcare encounters alone. This method can detect events which are missed by syndrome-oriented surveillance, but healthcare encounters that only have time of occurrence aren't necessarily related. To address this limitation, we propose a set of similarity criteria which incorporates both timing and reason.

Objective

Develop a method for detecting groups of related healthcare encounters without having to specify details of the reasons for those encounters in advance.

Previous reports from participating facilities in North Dakota illustrated that ILI syndrome data from syndromic surveillance data, which is based on chief complaints logs, had a close correlation to the traditional ILI surveillance and that frequency slope of the ILI syndrome was also closely correlated to that of the cases that tested positive for influenza. The facility used in this report submits ICD-9 codes to the North Dakota Department of Health (NDDoH). By comparing the NDDoH ILI syndrome to influenza laboratory testing data and ICD-9 code specific to influenza (487) we found that syndromic surveillance data for ILI closely followed the influenza testing trend as well as the ICD-9 code trend.

Objective

The objective of this report is to evaluate the correlation between influenza-like illness (ILI) syndrome classification using chief complaint data and discharge diagnosis International Classification of Disease, Ninth Revision (ICD-9) code for influenza with the laboratory data from one hospital in North Dakota over a period of three influenza seasons.

Following an Oct 12-13, 2006 snowstorm, almost 400,000 homes in western New York lost power, some for up to 12 days. News reports said that emergency rooms saw many patients with CO exposure; 3 deaths were attributed to CO poisoning. As part of NYS DOH’s syndromic surveillance system, electronic ED records with a free-text CC field listing the symptoms reported by the patient are sent to NYS DOH daily. Each CC is searched for text strings indicating complaints in one or more of 6 syndromes (asthma, fever, gastrointestinal (GI), neurological, respiratory, rash). The system also allows nonroutine searches of CCs for complaints of interest. NYS hospitals also submit ED records to the Statewide Planning and Research Cooperative System (SPARCS) that include diagnostic codes assigned after evaluation of the patient (due within 30 days of each calendar quarter).

Objective

To assess the ability to identify cases of carbon monoxide (CO) poisoning from chief complaints (CC) in hospital emergency department (ED) records submitted daily to the New York State (NYS) Department of Health (DOH) Electronic Syndromic Surveillance System.

In 2004, the BioDefend (BD) syndromic surveillance (SS) system was implemented in Duval County hospitals (Jacksonville, FL). Daily emergency department chief complaints are manually classified and entered into the BD system by triage personnel. As part of a statewide implementation, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) began collecting data in the Jacksonville area during the winter of 2007-08. ESSENCE uses an automated data collection, chief complaint parsing and analysis process for data management and analysis. The use of two systems during the same period of time in one area provided a unique opportunity to retrospectively analyze characteristics of the BD and ESSENCE systems.

Objective

To compare detection of a community outbreak of influenza-like illness using two SS systems, one using a clinician’s classification of reason for visit and the other using an automated chief complaint parsing algorithm.

ESSENCE is a web-based syndromic surveillance system utilized by DHMH to detect and track outbreaks, suspicious patterns of illness, public health emergencies, and biological threats. ESSENCE ED chief complaint data is collected daily from 47 emergency departments in Maryland (all 45 acute care hospitals and 2 freestanding emergency medical facilities). A chief complaint in ESSENCE is a free text field that lists the patient’s reason for the ED visit upon arrival at the hospital. Chief complaint data may be comprehensive or abbreviated and may include a single reason or multiple reasons for the ED visit. Medical history may be included in chief complaint data, which can create low specificity (false positive cases). Chief complaint data alone may yield less accurate modeling and lower outbreak detection sensitivity. This analysis evaluates whether counts of chief complaints are appropriate indicators of disease burden for several specific illnesses, by comparing chief complaints to their corresponding discharge diagnoses.

Objective

The state of Maryland has incorporated chief complaint data from 100% of its acute care emergency departments (ED) into the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012, the Maryland Department of Health and Mental Hygiene (DHMH) began using this statewide disease surveillance system to track several specific disease measures including certain chronic diseases. The validity of using ESSENCE ED data to track and analyze these health outcomes was evaluated.

The Electronic Surveillance System for the Early Notification of Community-based Epidemics in Florida (ESSENCE-FL) is a web-based application for use by public health professionals within the Florida Department of Health (FDOH). The main source of data for ESSENCE-FL is emergency department (ED) data. Ten hospitals in Hillsborough County, Florida send their data to the ESSENCE-FL server. ESSENCE-FL requires only a limited data set to be sent by the hospital which includes patient chief complaint (CC) and discharge diagnosis (DD). These fields can be searched individually, in separate queries, to identify possible records of interest. These two fields have been concatenated to create the single chief complaint and discharge diagnosis (CCDD) field, allowing both fields to be searched with a single query.

Objective

While syndromic surveillance systems were originally designed for the detection of outbreaks and clusters of illness, they have been found to be useful at identifying unreported conditions of public health importance. Within the Florida Department of Health in Hillsborough County (FDOH-Hillsborough), these conditions of public health importance have primarily focused on the reportable diseases and conditions that fall under the responsibility of the Epidemiology Program and have not included tuberculosis. A specific query has been developed to search for and identify possible tuberculosis patients and exposed contacts. This study is designed to determine the usefulness of specific-term chief complaint and discharge diagnosis (CCDD) queries in identifying tuberculosis patients and exposed contacts.

The CC text field is a rich source of information, but its current use for syndromic surveillance is limited to a fixed set of syndromes that are routine, suspected, expected, or discovered by chance. In addition to syndromes that are routinely monitored by the NYC Department of Health and Mental Hygiene (e.g., diarrhea, respiratory), additional syndromes are occasionally monitored when requested by outside sources or when expected to increase during emergencies. During Hurricane Sandy, we discovered by manual inspection of data for a few EDs an increase in certain words in the CC field (e.g., 'METHADONE', 'DIALYSIS', and 'OXYGEN') that led to the creation of a 'needs medication' syndrome. Current syndromic surveillance systems cannot detect unanticipated events that are not defined a priori by keywords. We describe a simple data-driven method that routinely scans the CC field for increases in word frequency that might trigger further investigation and/or temporary monitoring.

Objective

To detect sudden increases in word frequency in the Emergency Department (ED) syndromic chief complaint (CC) text field.

Abbreviation, misspellings, and site specific terminology may misclassify chief complaints syndromes. The Emergency Medical Text Processor (EMT-P) is system that cleans emergency department chief complaints and returns standard terms. However, little information is available on the implementation of EMT-P in a syndromic surveillance system.

Objective

To describe the implementation and baseline evaluation of EMT-P developed by the University of North Carolina.