Standards

In 2011, the Centers for Disease Control and Prevention (CDC) released the PHIN Messaging Guide for Syndromic Surveillance v. 1. In the intervening years, new technological advancements including Electronic Health Record capabilities, as well as new epidemiological and Meaningful Use requirements have led to the periodic updating and revision of the Message Guide. These updates occurred through informal and semi-structured solicitation and in response to comments from across public health, governmental, academic, and EHR vendor stakeholders. Following the Message Guide v.2.0 release in 2015, CDC initiated a multi-year endeavor to update the Message Guide in a more systematic manner and released further updates via an Erratum and a technical document developed with the National Institute of Standards and Technology (NIST) to clarify validation policies and certification parameters. This trio of documents were consolidated into the Message Guide v.2.1 release and used to inform the development of the NIST Syndromic Surveillance Test Suite (http://hl7v2-ss-r2-testing.nist.gov/ss-r2/#/home), validate test cases, and develop a new rules-based IG built using NIST's Implementation Guide Authoring and Management Tool (IGAMT). As part of a Cooperative Agreement (CoAg) initiated in 2017, CDC partnered with ISDS to build upon prior activities and renew efforts in engaging the Syndromic Surveillance Community of Practice for comment on the Message Guide. The goal of this CoAg is have the final product become an HL7 Standard for Trial Use following the second phase of formal HL7 balloting in Fall 2018.

Objective: To describe the latest revisions and modifications to the œHL7 2.5.1 Implementation Guide for Syndromic Surveillance (formerly the PHIN Message Guide for Syndromic Surveillance) that were made based on community commentary and resolution of feedback from the HL7 balloting process. In addition, the next steps and future activities as the IG becomes an HL7 Standard for Trial Use will be highlighted.

As the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (CDS). CDS for case reporting confronts two challenges: a) While EHRs are moving toward local CDS capabilities, it will take several years for EHR systems to consistently support this capability; and b) public health-related CDS knowledge, such as Zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. Therefore, there is an ongoing need to manage reporting rules in a distributed manner. Similarly, there is a need for more decentralized models of CDS execution to overcome some of the disadvantages of centralized deployment and to leverage local CDS capabilities as they become available over the next several years.

Objective: To discuss how clinical decision support (CDS) for electronic case reporting (eCR) will evolve over time to provide multiple deployment models

The U.S. Department of Homeland Security National Incident Management System (NIMS) establishes a common framework and common terminology that allows diverse incident management and support organizations to work together across a wide variety of functions and hazard scenarios1. Using common terminology helps avoid confusion and enhances interoperability, particularly in fast-moving public health (PH) emergency responses. In addition, common terminology allows diverse incident management and support organizations to work together across a wide variety of functions and scenarios1. LOINC is one of a suite of designated standards for the electronic exchange of public health and clinical information. Implementation of LOINC facilitates improvement of semantic interoperability, including unified terminology2. More than 68,100 registered users from 172 countries use LOINC to move interoperable data seamlessly between systems3. The CDC Division of Emergency Operations (DEO) leads development of standardized PH emergency preparedness and response terminology to improve effective and interoperable communications between national and international partners. Realizing the scale of LOINC support and implementation across the global public health arena, CDC DEO collaborates with LOINC to further enhance and harmonize the current PH emergency response terminology and to attain critical PH emergency management and preparedness and response requirements.

Objective: The purpose of this project is to demonstrate the progress in development of a standardized public health (PH) emergency preparedness and response data ontology (terminology) through collaboration between the Centers for Disease Control and Prevention (CDC), Division of Emergency Operations (DEO), and the Logical Observation Identifiers Names and Codes (LOINC) system.

As part of the French syndromic surveillance system SurSaUDî, the French Public Health Agency (Sant© publique France) collects daily data from the emergency department (ED) network OSCOUR®. The system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity. Individual ED data contain demographic (age, gender, residence zip code), administrative (dates of attendances and discharge, ED, etc.) and medical information (chief complaint, main and associated medical diagnoses, severity). Medical diagnoses are encoded using the ICD10 classification. Then syndromic groups are built based on these ICD10 codes for ensuring syndromic surveillance in routine. Even if ICD10 is recommended on the national guidelines for coding ED attendances, this thesaurus offers a too large variety of codes. Particularly, it includes lots of diseases that may never be observed or confirmed in ED. This variety let selection of the appropriate codes difficult for physicians in a reactive use and could discourage them to code diagnoses. In order to encourage appropriate and reactive coding practice, we decided in 2017 to produce a new diagnoses thesaurus with a limited list of ICD10 codes. Then a committee of medical and epidemiological experts was created by the Federation of regional emergency observatories (FedORU), to propose an operational thesaurus that includes relevant codes for both ED in a daily routine practice and syndromic surveillance.

Objective: The study aims to evaluate the potential impact of the revision of the thesaurus used by ED physicians to code medical diagnoses, on the syndromic indicators used daily to achieve the detection objective of the French syndromic surveillance system.

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.