Real-time Surveillance

The Office of the Medical Examiner (OME) is a statewide system for investigation of sudden and unexpected death in Utah. OME, in the Utah Department of Health (UDOH), certified over 2000 of the 13,920 deaths in Utah in 2008. 

Information from OME death investigations is currently stored in three separate UDOH data silos that have limited interoperability. These three electronic data systems include death certificates, medical examiner investigations, and laboratory results. Without interoperability, OME staff is required to enter the same data into multiple systems. In addition, the process of requesting laboratory analysis and receiving results is paper based, significantly slowing final cause of death determination in a majority of cases. 

Epidemiological studies and surveillance activities are hindered by the lack of systems integration in UDOH and often require retrospective data linkage. As an example, in 2005, CDC and the UDOH reported that deaths in Utah caused by drug poisoning from non-illicit drugs had increased fivefold from 1991 to 2003. This significant finding relied on retrospective linkage of death certificates, medical examiner records, and toxicology results to describe the problem.

In 2008, funding from a bioterrorism grant from the US Department of Homeland Security was secured to support development of a unique, integrated system for medical examiner and death certificate data.

Objectives

The objectives of the Utah Medical Examiner Database project are: 

• To provide a single point of entry for medical examiner pathologists and staff to manage investigation information. 
• To develop an operational system that links death certificate, medical examiner, and laboratory data in real time as a resource for epidemiology and public health surveillance.

Group A Streptococcal (GAS) pharyngitis, the most common bacterial cause of acute pharyngitis, causes more than half a billion cases annually worldwide. Treatment with antibiotics provides symptomatic benefit and reduces complications, missed work days and transmission. Physical examination alone is an unreliable way to distinguish GAS from other causes of pharyngitis, so the 4-point Centor score, based on history and physical, is used to classify GAS risk. Still, patients with pharyngitis are often misclassified, leading to inappropriate antibiotic treatment of those with viral disease and to under-treatment of those with bone fide GAS. One key problem, even when clinical guidelines are followed, is that diagnostic accuracy for GAS pharyngitis is affected by earlier probability of disease, which in turn is related to exposure. Point-of-care clinicians rarely have access to valuable biosurveillance-derived contextualizing information when making clinical management decisions.

Objective

The objective of this study was to measure the value of integrating real-time contemporaneous local disease incidence (biosurveillance) data with a clinical score, to more accurately identify patients with GAS pharyngitis.

Syndromic surveillance has been widely adopted as a real-time monitoring tool in early response to disease outbreaks. In order to provide real-time information on the impact of 2009 H1N1 during the Fall 2009 semester, Georgetown University (GU) and George Washington University (GWU) employed syndromic surveillance systems incorporating a variety of data sources. 

Objective

To describe the 2009 H1N1 outbreak at GU and GWU in Fall 2009. Identify the datasets that most accurately depict 2009 H1N1 disease in real time.

Surveillance of deaths due to influenza and pneumonia using death records has the potential to be a relatively inexpensive and quick approach to tracking and detecting influenza and respiratory illness outbreaks; however, presently such a system does not exist because of the time delays in processing death records: in Utah, as is similar elsewhere in the United States, coded death certificate data are typically not available for at least 1–3 months after the date of death, and coded national vital statistics data are not available until after 2–3 years.

Objective

This poster presents the rationale for designing a real-time surveillance system, based on mortality data, using grid and natural language processing tools that will address the current barrier that coded death certificate data not being available for several months. To develop a public health tool that delivers a timely surveillance system for influenza and pneumonia, we integrated death certificates from the Utah Department of Health, analytical grid services, and natural language processing tools to monitor levels of mortality. This example demonstrates how local, state, and national authorities can automate their influenza and pneumonia surveillance system, and expand the number of reporting cities.

Current influenza-like illness (ILI) monitoring in Idaho is based on syndromic surveillance using laboratory data, combined with periodic person-to-person reports collected by Idaho state workers. This system relies on voluntary reporting.

Electronic medical records offer a method of obtaining data in an automated fashion. The Computerized Patient Record System (CPRS) captures real-time visit information, vital signs, ICD-9, pharmacy, and lab data. The electronic medical record surveillance has been utilized for syndromic surveillance on a regional level. Funds supporting expansion of electronic medical records offer increased ability for use in biosurveillance. The addition of temporo-spatial modeling may improve identification of clusters of cases. This abstract reviews our efforts to develop a real-time system of identifying ILI in Idaho using Veterans Administration data and temporo-spatial techniques.

Objective

The objective of this study is to describe initial efforts to establish a real-time syndromic surveillance of ILI in Idaho, using data from the Veterans Administration electronic medical record (CPRS).

Modern information and communication technologies have increasingly prominent roles in health care systems. To capitalize on attainable benefits, it is essential to thoroughly and purposefully weave them into the existing business processes. The challenges of doing so can be exacerbated by specific local circumstances of developing countries. We

share our experiences from fielding a system designed to support real-time collection and analysis of public health data in rural areas of Sri Lanka and India. Its strong transformational potential has been proven, however, success of the ultimate field use requires overcoming multiple organizational and utility challenges.

Objective

We review challenges faced during the initial period of implementation of a Real-Time Biosurveillance Program in developing countries.

We are developing a Bayesian surveillance system for realtime surveillance and characterization of outbreaks that incorporates a variety of data elements, including free-text clinical reports. An existing natural language processing (NLP) system called Topaz is being used to extract clinical data from the reports. Moving the NLP system from a research project to a real-time service has presented many challenges.

Objective

Adapt an existing NLP system to be a useful component in a system performing real-time surveillance.

Current practices of automated case detection fall into the extremes of diagnostic accuracy and timeliness. In regards to diagnostic accuracy, electronic laboratory reporting (ELR) is at one extreme and syndromic surveillance is at the other. In regards to timeliness, syndromic surveillance can be immediate, and ELR is delayed 7 days from initial patient visit. A plausible solution, a middle way, to the extremes of diagnostic precision and timeliness in current case detection practices is an automated Bayesian diagnostic system that uses all available data types, for example, freetext ED reports, radiology reports, and laboratory reports.We have built such a solution - Bayesian case detection (BCD). As a probabilistic system, BCD operates across the spectrum of diagnostic accuracy, that is, it outputs the degree of certainty for every diagnosis. In addition, BCD incorporates multiple data types as they appear during the course of a patient encounter or lifetime, with no degradation in the ability to perform diagnosis.

Objective

This paper describes the architecture and evaluation of our recently developed automated BCD system.

Real-Time Biosurveillance Program (RTBP) introduces modern surveillance technology to health departments in Sri Lanka and Tamil Nadu, India. Triage data from each patient visit (basic demographics, signs, symptoms, preliminary diagnoses) is recorded on paper at health facilities. Case records are transmitted daily to a central database using the RTBP mobile phone application. It is done by medical professionals in India, but in Sri Lanka, due to staffing constraints, the same duty is performed by lower cost personnel with limited domain knowledge. That results in noticeable differences in data entry error rates between the two locations. Most of such issues are due to systematic and subjectivemisinterpretations of the handwritten doctor notes by the data entry personnel. If not identified and remedied quickly, these errors can adversely affect accuracy and timeliness of health events detection. There is a need to support system managers in their efforts to maintain high reliability of data used for public health surveillance.

Objective

We present a method for automated detection of systematic data entry errors in real time biosurveillance.

The 2009 H1N1 novel flu pandemic demonstrates how a rapidly spreading, contagious illness can affect the world’s population in multiple ways including health, economics, education, transportation, and national security. Pandemic disease and the threat of bio-terrorism are prompting the need for a system that integrates disparate data, makes optimal use of the breadth of available health-related analysis and predictive models, and provides timely guidance to decision makers at multiple levels of responsibility.

Objective

Traditional real time surveillance systems such as RODS and ESSENCE have focused on the task of threat detection; however, experience with the use of these systems in pandemic and disaster response settings suggests that a more common application is threat characterization and response management. This paper describes EpiSentry: a novel second generation real-time surveillance software system under development at Lockheed Martin that uses simulation to aid in threat characterization, response management and to provide decision support for disease outbreaks or bio-terror events.