Electronic Health Records

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.

The purpose of this study is to investigate the use of electronic medical record (EMR) data sources to improve the detection performance of a syndromic surveillance system. This analysis involves examining the temporal correlation between alerts generated from the EMR data sources and alerts generated from the more traditional data sources already being used by the surveillance system.

This paper describes and compares electronic systems used by the Department of Defense (DoD) for syndromic surveillance in-garrison and in a deployed environment in Southwest Asia.

This paper describes three years of electronic health record (EHR) data from a network of urban ambulatory care clinics in New York City.

Approximately one quarter of people treated for tuberculosis (TB) have no supporting microbiology, and thus are not detectable through laboratory reporting systems. Health departments depend upon clinicians to report these cases, but there is important underreporting. We previously described the performance of several algorithms for TB detection using electronic medical record (EMR) and claims data, and noted good sensitivity when screening for >2 anti-TB drugs; however, the positive predictive value was only 30%. We re-evaluated this and other algorithms in light of evolving TB treatment practices and enhanced ability to apply complex decision rules to EMR data in real time.

Objective

To develop algorithms for case detection of TB using EMR data to improve notifiable disease reporting.

Objective

We performed a gold-standard manual chart review for gastro-intestinal syndrome to evaluate automated detection models based on both structured and non-structured data extracted from the VA electronic medical record.

1) Describe a near real-time school-based syndromic surveillance program that integrates electronic data records and a two-way health alert system for early outbreak detection, notification, and possible intervention for Arizona schools. 2) Demonstrate the public health utility of this system for early detection of influenza among school children.

To describe how the Miami-Dade County Health Department (MDCHD) has expanded Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE) for specialized research in addition to daily surveillance activities

Objective: To enable improved health surveillance and clinical decision support within ambulatory Electronic Health Record (EHR) systems.