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ESSENCE

Description

Syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.

Objective

To assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.

Submitted by teresa.hamby@d… on
Description

Antimicrobial prescriptions are a new data source available to the Veterans Health Administration (VHA) biosurveillance program. Little is known about whether antiviral or antibacterial prescription data correlates with influenza ICD-9-CM coded encounters. We therefore evaluated the utility and timeliness of antiviral and antibacterial utilization for influenza surveillance.

Submitted by teresa.hamby@d… on

The homelessness syndrome was developed to identify emergency department visits in ESSENCE for patients who are experiencing homelessness or housing insecurity. The syndrome is intended for use with chief complaint, triage notes, and discharge diagnosis codes (ICD-10 CM). The definition heavily relies on diagnosis codes primarily used by non-critical access hospitals and artificial exclusion of critical access facilities should be considered when data are interpreted.

Submitted by Anonymous on

This query is used to assess trends in hypothermia or cold exposure in emergency department visits in ESSENCE. The query captures cold exposure, hypothermia, and frost bite using chief complaint, triage note, and discharge diagnosis code (ICD-10CM). The query does not exclude hypothermia related to an underlying medical condition.

Submitted by Anonymous on

In late summer 2017, the United States endured two severe hurricanes back to back. On August 25, 2017, Hurricane Harvey made landfall in Texas and southwest Louisiana, dumping more than 19 trillion gallons of rain. On September 10, 2017, 20 days later, Hurricane Irma landed in Florida, leading residents across the Florida peninsula to evacuate inland and out of the path of the storm. Although Tennessee was far from the eye of the storms, state health officials knew residents from both states could choose to shelter in Tennessee.

Submitted by elamb on
Description

Objective:

The objective of this project is to enable the ESSENCE system to read in, utilize, and export out meaningful use syndromic surveillance data using the Health Level 7 (HL7) v2.5 standard. This presentation will detail the technical hurdles with reading a meaningful use syndromic surveillance data feed containing multiple sources, deriving a common meaning from the varying uses of the standard and writing data out to a meaningful use HL7 2.5 format that can be exported to other tools, such as BioSense 2.0 (2). The presentation will also describe the technologies employed for facilitating this, such as Mirth, and will discuss how other systems could utilize these tools to also support processing meaningful use syndromic surveillance data.

Introduction:

In order to utilize the new meaningful use syndromic surveillance data sets that many public health departments are now receiving, modifications to their systems must be made. Typically this involves enabling the storage and processing of the extra fields the new standard contains. Open source software exists, such as Mirth Connect, to help with reading and interpreting the standard. However, issues with reliably reading data from one source to another arise when the standard itself is misunderstood. Systems that process this data must understand that while the data they receive is in the HL7 v2.5 standard format, the meaning of the data fields might be different from provider to provider. Additional work is necessary to sift thro

Submitted by jababrad@indiana.edu on
Description

As system users develop queries within ESSENCE, they step through the user-interface to select data sources and parameters needed for their query. Then they select from the available output options (e.g., time series, table builder, data details). These activities execute a SQL query on the database, the majority of which are saved in a log so that system developers can troubleshoot problems. Secondarily, these data can be used as a form of web analytics to describe user query choices, query volume, query execution time, and develop an understanding of ESSENCE query patterns.

Objective:

The objective of this work is to describe the use and performance of the NSSP ESSENCE system by analyzing the structured query language (SQL) logs generated by users of the National Syndromic Surveillance Program'™s (NSSP) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).

Submitted by elamb on
Description

The Kansas Syndromic Surveillance Program (KSSP) utilizes the ESSENCE v.1.20 program provided by the National Syndromic Surveillance Program to view and analyze Kansas Emergency Department (ED) data. Methods that allow an ESSENCE user to query both the Discharge Diagnosis (DD) and Chief Complaint (CC) fields simultaneously allow for more specific and accurate syndromic surveillance definitions. As ESSENCE use increases, two common methodologies have been developed for querying the data in this way. The first is a query of the field named “CC and DD.” The CC and DD field contains a concatenation of the parsed patient chief complaint and the discharge diagnosis. The discharge diagnosis consists of the last non-null value for that patient visit ID and the chief complaint parsed is the first non-null chief complaint value for that patient visit ID that is parsed by the ESSENCE platform. For this comparison, this method shall be called the CCDD method. The second method involves a query of the fields named, Chief Complaint History and œDischarge Diagnosis History. While the first requires only one field be queried, this method queries the CC History and DD History fields, combines the resulting data and de-duplicates this final data set by the C_BioSense_ID. Chief Complaint History is a list of all chief complaint values related to a singular ED visit, and Discharge Diagnosis History is the same concept, except involving all Discharge Diagnosis values. For this comparison, this method shall be called the CCDDHX method. While both methods are based on the same query concept, each method can yield different results.

Objective:

To compare and contrast two ESSENCE syndrome definition query methods and establish best practices for syndrome definition creation.

Submitted by elamb on
Description

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts syndromic surveillance of Emergency Department (ED) visits through the Louisiana Early Event Detection System (LEEDS) and submits the collected data to ESSENCE. There are currently 86 syndromes defined in LEEDS including infectious disease, injury and environmental exposure syndromes, among others. LEEDS uses chief complaint, admit reason, and/or diagnosis fields to tag visits to relevant syndromes. Visits that do not have information in any of these fields, or do not fit any syndrome definition are tagged to Null syndrome. ESSENCE uses a different algorithm from LEEDS and only looks in chief complaint for symptom information to bin visits to syndromes defined in ESSENCE. Visits that do not fit the defined syndromes or do not contain any symptom information are tagged to Other syndrome. Since the transition from BioSense to ESSENCE, IDEpi has identified various data quality issues and has been working to address them. The NSSP team recently notified IDEpi that a large number of records are binning to Other syndrome, which led to the investigation of the possible underlying data quality issues captured in Other syndrome.

Objective:

This investigation takes a closer look at Other syndrome in ESSENCE and Null syndrome in LEEDS to understand what types of records are not tagged to a syndrome to elucidate data quality issues.

Submitted by elamb on
Description

Oregon Public Health Division (OPHD), in collaboration with The Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2011. ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data from hospitals across Oregon. While each hospital system sends HL7 2.5.1-formatted messages, each uses a uniquely configured interface to capture, extract, and send data. Consequently, ESSENCE receives messages that vary greatly in content and structure. Emergency department data are ingested using the Rhapsody Integration Engine 6.2.1 (Orion Health, Auckland, NZ), which standardizes messages before entering ESSENCE. Mechanisms in the ingestion route (error-handling filters) identify messages that do not completely match accepted standards for submission. A sub-set of these previously-identified messages with errors are corrected within the route as they emerge. Existence of errors does not preclude a message’s insertion into ESSENCE. However, the quality and quantity of errors determine the quality of the data that ESSENCE uses. Unchecked, error accumulation also can cause strain to the integration engine. Despite ad-hoc processes to address errors, backlogs accrue. With no meta-data to assess the importance and source of backlogged errors, the ESSENCE team had no guide with which to mitigate errors. The ESSENCE team needed a way to determine which errors could be fixed by updating the Rhapsody Integration Engine and which required consultation with partner health systems and their data vendors. To formally address these issues, the ESSENCE team developed an error-capture module within Rhapsody to identify and quantify all errors identified in syndromic messages and to use as a guide to prioritize fixing new errors.

Objective:

To streamline emergency department data processing in Oregon ESSENCE (Oregon’s statewide syndromic surveillance) by systematically and efficiently addressing data quality issues among submitting hospital systems.

Submitted by elamb on