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Data Integration

The Oregon ESSENCE team has developed a guide for other states to use to set up a web service link to their poison center and extract its data into ESSENCE. It contains advice based on Oregon’s experience in developing its link with its poison center and NDPS, a plug-&-play (almost) Rhapsody configuration file (and instructions) to install, and data dictionaries provided by NPDS.

The publication date is February 1, 2019.

Submitted by ctong on
Description

A comprehensive definition of a syndrome is composed of direct (911 calls, emergency departments, primary care providers, sensor, veterinary, agricultural and animal data) and indirect evidence (data from schools, drug stores, weather etc.). Syndromic surveillance will benefit from quickly integrating such data. There are three critical areas to address to build an effective syndromic surveillance system that is dynamic, organic and alert, capable of continuous growth, adaptability and vigilance: (1) timely collection of high quality data (2) timely integration and analysis of information (data in context) (3) applying innovative thinking and deriving deep insights from information analysis. In our view there is excessive emphasis on algorithms and applications to work on the collected data and insufficient emphasis on solving the integration challenges. Therefore, this paper is focused on information integration.

Objective

EII is the virtual consolidation of data from multiple systems into a unified, consistent and accurate representation. An analyst working in an EII environment can simultaneously view and analyze data from multiple data sources as if it were coming from one large local data warehouse. This paper posits that EII is a viable solution to implement a system covering large areas and disparate data sources for syndromic surveillance and discusses case studies from environments external to health.

Submitted by Sandra.Gonzale… on
Description

Bordetella Pertussis outbreaks cause morbidity in all age groups, but the infection is most dangerous for young infants. Pertussis is difficult to diagnose, especially in its early stages, and definitive test results are not available for several days. Because of temporal and geographic variability of pertussis outbreaks, delay in diagnostic test results and ramifications of incorrect management decisions at the point of care, pertussis represents a prototypical disease where realtime public health surveillance data might inform, guide and improve medical decision making. Previously, we showed that diagnostic accuracy for meningitis can be improved when information about recent, local disease incidence is accounted for. Here, we quantify the contribution of epidemiologic context to a clinical prediction model for pertussis using a state public health data stream.

 

Objective

To explore the integration of epidemiological context – current population-level disease incidence data – into a clinical prediction model for pertussis.

Submitted by elamb on
Description

In the aftermath of September 11th, 2001, the potential for subsequent bioterrorism attacks and more recently, the increased awareness of the threat of Avian flu and other communicable diseases, has compelled the Montana healthcare community to mobilize its diagnostic resources for detecting the presence of toxins or infectious biologic agents at the earliest possible moment. This state-wide, pilot initiative integrates disparate Emergency Room data, making patients’ symptoms and diagnoses available for biosurveillance and achieves interoperability among Montana’s emergency facilities.

 

Objective

This oral presentation describes a multi-agency and multi-center medical data integration system for syndromic surveillance in the State of Montana. This is a significant public health benefit given the recent threats of bio-terrorism and potential viral epidemics, including Bird-Flu.

Submitted by elamb on
Description

This paper outlines the integration of hospital admission, Febrile Respiratory Illness (FRI) screening and Canadian Triage and Acuity Score (CTAS) data streams within an Emergency Department Syndromic Surveillance system. These data elements allow better characterization of outbreak severity and enable more effective resource allocation within acute care settings.

Submitted by elamb on
Description

Objective: Emerging and re-emerging infectious diseases (EID/REID) involve large populations at risk and thus they might lead to rapidly increasing cases or case fatality rates. Living in this global village, cross-country or cross-continent spread has occurred more frequently in recent decades, implying that epidemics of any infectious disease can expand from local to national to international if control efforts are not effective.

Submitted by elamb on
Description

Traditionally Emergency Department syndromic surveillance methods have relied on ICD-9 codes and chief complaints. The implementation of electronic medical record keeping has made much more information available than can potentially be used for surveillance. For example, information such as vital signs, review of systems and physical exam data are being stored discreetly. These data have the potential to detect specific diseases or outbreaks in a community earlier that the traditionally used ICD-9 and chief complaint.

 

Objective

This paper describes the integration of novel data sets from an Emergency Department Electronic Medical Record into a syndromic surveillance application.

Submitted by elamb on
Description

This presentation introduces the U.S. Department of Homeland Security (DHS) National Bio-Surveillance Integration System (NBIS) and the analytics functionality within the NBIS that integrates and analyzes structured and unstructured data streams across domains to provide inter-agency analysts with an integrated view of threat scenarios. The integration of Human and Animal incidences of Avian Influenza will be used to demonstrate initial capability.

Submitted by elamb on
Description

Integration of information from multiple disparate and heterogeneous sources is a labor and resource intensive task. Heterogeneity can come about in the way data is represented or in the meaning of data in different contexts. Semantic Web technologies have been proposed to address both representational and semantic heterogeneity in distributed and collaborative environments. We introduce an automated semantic information integration platform for public health surveillance using RDF and the Simple Knowledge Organization Standard developed by the Semantic Web community.

 

OBJECTIVE

This paper proposes the use of Semantic Web technologies to integrate heterogeneous data generated by disparate systems for public health use.

Submitted by elamb on
Description

The ability to provide real time syndromic surveillance throughout the Capital Health Region is currently undeveloped. There are limited mechanisms for routine real time surveillance of disease or conditions of public health interest, e.g. communicable diseases, toxic exposure or injury. Toxic exposure and injury while preventable are not notifiable in Alberta and as a consequence there is no real-time surveillance system to identify burden of disease or opportunities for intervention. The notifiable disease system is reliant on paper-based forms which are slow, prone to human error, and labor intensive to convert to electronic database format for flexible analysis and interpretation. Finally there is no system to link the data collected on the same individual in each database without compromising confidentiality. ARTSSN is designed to remedy these deficiencies.

 

Objective

In this presentation we describe the creation of an IT architecture and infrastructure to integrate data from four sources to support real-time syndromic surveillance for injuries, toxic exposures and notifiable diseases in Capital Health, Alberta, Canada.

Submitted by elamb on