Electronic Tools

Domains go through phases of existence, and the electronic disease surveillance domain is no different. This domain has gone from an experimental phase, where initial prototyping and research tried to define what was possible, to a utility phase where the focus was on determining what tools and data were solving problems for users, to an integration phase where disparate systems that solve individual problems are tied together to solve larger, more complex problems or solve existing problems more efficiently. With the integration phase comes the desire to standardize on many aspects of the problem across these tools, data sets, and organizations. This desire to standardize is based on the assumption that if all parties are using similar language or technology then it will be easier for users and developers to move them from one place to another.

Normally the challenge to the domain is deciding on a vocabulary or technology that allows seamless transitions between all involved. The disease surveillance domain has accomplished this by trying to use some existing standards, such as HL7, and trying to develop some of their own, such as chief complaint-based syndrome definitions. However, the standards that are commonly discussed in this domain are easily misunderstood. These misunderstandings are predominantly a communication and/or educational issue, but they do cause problems in the disease surveillance domain. With the increased use of these standards due to meaningful use initiatives, these problems will continue to grow and be repeated without improved understanding and better communication about standards.

Objective

This talk will point out the inconsistencies and misunderstandings of the word "standard". Specifically, it will discuss HL7, syndrome definitions, analytical algorithms, and disease surveillance systems.

Developing countries bear the highest burden of infectious diseases, and therefore play a key role in the detection of emerging disease threats. The ability of these countries to detect such events allows an adequate response preventing its spread and reducing the morbidity and mortality of communities across the world. Due to the importance of strengthening the surveillance capabilities of developing countries, resources need to be dedicated to this effort. International organizations often provide technical and financial support when countries need assistance. When implementing such systems, the countries and supporting organizations face challenges not limited to scarce resources. These countries often also face political and social instability, and cultural and religious practices that may put their populations at greater risk of these events. In addition, the disease surveillance field lacks clear guidance on how to initiate or improve the implementation process for these systems. The experiences described in the literature are often presented as partial evaluations using the US Centers for Disease Control and Prevention guidelines, without providing an explanation of the key factors that are related to successful or unsuccessful experiences in such settings. Public health practitioners at the ministries of health (MOH) and international organizations would greatly benefit from a model that considers these factors to serve as a guideline for a positive implementation outcome and an effective use of resources.

Objective

The panel will present successful and unsuccessful practices, challenges and lessons learned when implementing electronic surveillance tools in the developing world, and a best practices model that aims to guide and facilitate this process. Panelists and audience will discuss the validity of this model, and identify potential countries where it could be tested.

The Automated Hospital Emergency Department Data System is designed to detect early indicators of bioterrorism events and naturally occurring public health threats. Four investigatory tools have been developed with drill-down detail reporting: 1. Syndromic Alerting, 2. Chief Complaint Data Mining, 3. ICD9 Code Disease, and 4. Influenza-Like-Illness Tracking.

All analysis processing runs on the server in seconds using ORACLE PL/SQL stored procedures and arrays.

Objective

This paper details the development of electronic surveillance tools by Communicable Disease Surveillance, which have increased detection and investigation capabilities.

Advanced surveillance systems require expertise from the fields of medicine, epidemiology, biostatistics, and information technology to develop a surveillance application that will automatically acquire, archive, process and present data to the user. Additionally, for a surveillance system to be most useful, it must adapt to the changing environment in which it operates to accommodate emerging public health events that could not be conceived of when the initial system was developed.

Objective

The objective of this presentation is to describe both within-discipline and across-discipline changes to standard methods and operating procedures that must be adopted to achieve automated systems that will be an effective complement and extension to traditional disease surveillance. This presentation describes adaptations already in place, as well as those still needed to rapidly recognize and respond to public health emergencies.

Public health officials are now receiving more data than ever in electronic formats, and also stand to benefit more than ever from ongoing advances in the medical and epidemiological sciences. At the same time, this growing body of knowledge as well as volatile world events present an increasingly complex set of threats to population health. As a consequence, public health officials are finding that they need to ask many more, and more complex, questions of their data in order to keep sight of the state of the public’s health. Most current disease surveillance systems enable users to ask many different questions of health data, but are limited in that users can only extract results one question, or query, at a time.

Objective

Develop an Automated Data Query tool to allow public health officials to easily extract batches of raw medical encounter data using custom queries that the officials themselves set up. Additionally, the tool shall be capable of running anomaly detection algorithms against the raw data and returning the statistics. Users shall be able to perform their own analyses on the data and/or the statistical results after using the tool to collect the information efficiently. The tool will help them spot trends of interest that may be specific to their own jurisdictions.

A syndromic surveillance system has been implemented at Kingston, Frontenac and Lennox & Addington Public Health in Kingston Ontario as part of a pilot project funded by the Ontario Ministry of Health and Long-Term Care – Public Health Division. The information captured by the Real-time Outbreak and Disease Surveillance-based syndromic surveillance system includes Febrile Respiratory Illness screening results (implemented since SARS) for Emergency Department (ED) visits and information detailing hospital admissions.

Objective

To use an electronic real-time ED monitoring tool to involve public health, acute care and laboratory stakeholders in an integrated alerting and response process for community-wide influenza.

Our toolkit adds statistical trend analysis, interactive plots, and kernel density estimation to an existing spatio-temporal visualization platform. The goal of these tools is to provide both a quick assessment of the current syndromic levels across a large area and then allow the analyst to view the actual data for a specific region or hospital over a period of time along with an indication as to whether or not a given data point is statistically significant. The sample data used for this toolkit come from over 70 emergency rooms throughout the state of Indiana.

Objective

This paper presents a toolkit designed to aid in the assessment of disease outbreak by visualizing spatiotemporal trends and interactively displaying detailed statistical data.