Clinician initiated reporting of notifiable conditions is often delayed, incomplete, and lacking in detail. We report on the deployment of Electronic medical record Support for Public health (ESP), a system we have created to automatically screen electronic medical record (EMR) systems for evidence of reportable diseases, to securely transmit disease reports to health authorities, and to respond to queries from health departments for clinical details about laboratory detected cases. ESP consists of software that constructs and analyzes a temporary database that is regularly populated with comprehensive codified encounter data from a medical practice's EMR system. The ESP database resides within the host medical practice's firewall, configured on either a central workstation to service large multi-site, multi-physician practices or as a software module running alongside a small practice's EMR system on a personal computer. The encounter data sent to ESP includes patient demographics, diagnostic codes, laboratory test results, vital signs, and medication prescriptions. ESP regularly analyzes its database for evidence of notifiable diseases. When a case is found, the server initiates a secure Health Level 7 message to the health department. The server is also able to respond to queries from the health department for demographic data, treatment information, and pregnancy status on cases independently reported by electronic laboratory systems. ESP is designed to be compatible with any EMR system with export capability: it facilitates translation of proprietary local codes into standardized nomenclatures, shifts the analytical burden of disease identification from the host electronic medical record system to the ESP database, and is built from open source software. The system is currently being piloted in Harvard Vanguard Medical Associates, a multi-physician practice serving 350,000 patients in eastern Massachusetts. Disease detection algorithms are proving to be robust and accurate when tested on historical data. In summary, ESP is a secure, unobtrusive, flexible, and portable method for bidirectional communication between EMR systems and health departments. It is currently being used to automate the reporting of notifiable conditions but has promise to support additional public health objectives in the future.
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.