Standard

Cost-effective, flexible and innovative tools that integrate disparate data sets and allow sharing of information between geographically dispersed collaborators are needed to improve public health surveillance practice. Gossamer Health (Good Open Standards System for Aggregating, Monitoring and Electronic Reporting of Health), http://gossamerhealth.org, is an open source system, suitable for server or "cloud" deployment, that is designed for the collection, analysis, interpretation and visualization of syndromic surveillance data and other indicators to monitor population health. The Gossamer Health system combines applied public health informatics research conducted at the University of Washington Center for Public Health Informatics and Washington State Department of Health, in collaboration with other state and local health jurisdictions, the International Society for Disease Surveillance and the Centers for Disease Control and Prevention.

Objective

The goal of this work is to make available to the public health community an open source system that makes available in a standards-based, modular fashion the basic tools required to conduct automated indicator-based population health surveillance. These tools may be deployed in a flexible fashion on health department servers, in the Amazon EC2 cloud, or in any combination, and are coupled through well-defined standards-based interfaces.

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.

The Decrease Epidemiological Threats with Environmental Controls and Testing (DETECT) program consisted of a four month initiative to provide a two-day in-depth training modified from the National Environmental Health Association (NEHA) Epi-Ready training. In each Public Health Area, Central Office Epidemiology (EPI), Environmental, and Laboratory staff presented FBO information to county and area Public Health staff. To train and equip field staff in the management of foodborne outbreak processes, to establish a Foodborne Outbreak (FBO) Team in each Public Health Area, to establish defined processes and protocols for FBO for improved response.

Lack of speed, reliability, and uniformity of data collection limit the ability of syndromic surveillance (SyS) systems to provide public health authorities (PHAs) with timely information on community health threats and trends. Electronic information technologies have long been used to accelerate and automate data collection for more real-time surveillance. There is, however, irregularity in how SyS data are packaged and sent by healthcare providers. In the US, federal programs to improve patient and population health outcomes are promoting enhanced EHR technology interoperability with a newly mandated standard for SyS data communication. Under a federal rule tied to Medicare and Medicaid reimbursement rates, hospitals are now required to provide SyS data to PHAs using HL7 2.5.1 messages that are in conformance with the CDC’s Public Health Information Network (PHIN) guide for SyS. Merely mandating this standard, however, does not necessarily mean that it will be used as intended or that EHR SyS data will be interoperable among systems. Technology standards are frequently implemented with inconsistencies that spring from guidance ambiguities or misinterpretations. Inserting thorough conformance testing early in the technology development life cycle can increase the probability of conformance to standards, interoperability, and product reliability while reducing overall costs.

Objective

Describe how the National Institute of Standards and Technology's (NIST) Syndromic Surveillance Messaging Validation Suite supports federal efforts to increase electronic health record (EHR) interoperability for timelier public health surveillance capabilities in the US.

Meaningful Use has increased interest in submission of ELR to public health agencies, prompting these agencies to analyze their reporting process. Tennessee’s reporting regulations require anyone with knowledge of or suspecting a reportable disease or event report to the local health department. Although it is understood that laboratories are more diligent and routine reporters, focus in listing of these events is from the healthcare provider perspective. Public health agencies must acknowledge the differences in provider case reporting and laboratory result reporting. Despite Tennessee Department of Health's (TDH) required use of standardized vocabulary for ELR such as Logical Observation Identifiers Names and Codes (LOINC) to identify the test performed and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) to identify organism names, ordinal results, and specimen type, internally inconsistent information in messages has been identified. For example, a performed test with LOINC value 13950-1 encodes for a hepatitis A virus IgM antibody test from serum or plasma using an enzyme immunoassay (EIA) and calls for an ordinal result. However the sender describes a Hepatitis C Antibody (Anti HCV) test and provides a numeric result. In order to achieve semantic understanding of the actionable content of ELR messages, a systematic means to document and validate vocabulary is needed.

Objective

To develop a means for validating standardized vocabulary used to report laboratory events via prescribed electronic laboratory reporting (ELR) standards and implementation guides in order to limit internally inconsistent information within ELR messages intended for public health action.

The goal of this paper is to describe a methodology used to create a gold standard set of emergency department (ED) data that can subsequently be used to evaluate the sensitivity and specificity of syndrome definitions.