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04/25/2005 11:15 PM
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FIGURE 5.6 Cont'd b, Screenshot from a demonstration of how a clinician report of hepatitis A would have been signed by a clinician during the hepatitis A outbreak described in Chapter 1 (Pennsylvania Department of Health, 2005).
healthcare system to local and state health departments, investigations of reported cases, and reporting of cases to the CDC.
The NEDSS specifications include both architectural specifications (discussed in Chapter 33) and functional requirements. The architectural specifications cover eight elements: (1) Web browser-based data entry and management; (2) electronic HL-7 message processing; (3) integrated data repository, defining a single point of entry for receipt of data reported to the health department; (4) active data translation and exchange; (5) contemporary application programming practices, which permit, for example, the implementation of case definition logic at appropriate points; (6) data reporting and visualization, including the use of commercial off-the-shelf packages for analytical work and reporting capacity appropriate to different user groups; (7) shareable directory of public health personnel; and (8) consistent security standards to maintain the public health track record in protecting sensitive data (CDC, 2005d).
As discussed in Chapter 3, a variety of terms, including early warning and syndromic surveillance, have been used to refer to biosurveillance systems that attempt to detect outbreaks based on prediagnostic data (Mandl et al., 2004).
In 2003, approximately 100 sites throughout the country had implemented some form of syndromic surveillance (Buehler et al., 2003). The number of sites has increased since then.
Currently available syndromic surveillance systems, such as RODS (Wagner et al., 2004) and ESSENCE (Lombardo et al., 2004) use emergency department free-text chief complaints to group patient symptoms into syndromes. The systems aggregate counts of syndromes by day and monitor over time for statistical increases, which may provide early indication of an outbreak or potential bioterrorist event. RODS and ESSENCE are used by many health departments (Tsui et al., 2003; Henry et al., 2004). ESSENCE is used by the U.S. military globally. The RODS software is available as open source software under the GNU General Public License (downloadable from openrods.sourceforge.net). New York City (Das et al., 2003; Moran and Taln, 2003), Connecticut (Dembek et al., 2004) and Oregon (Townes et al., 2004) have implemented their own syndromic surveillance systems.
The Early Aberration Reporting System (EARS) (Hutwagner et al., 2003) is software that runs on a personal computer and allows epidemiologists to analyze syndromic data (unlike RODS and ESSENCE, EARS does not automate data collection from hospitals and other organizations). EARS is used by many local health departments.
BioSense is a syndromic surveillance system operated by the CDC (Loonsk, 2004). Biosense is one of three similarly named federal initiatives (the other two are Biowatch and Bioshield) to advance national bioterrorism preparedness. At present, Biosense collects ICD-9 coded registration data from the VA
and the Department of Defense, and laboratory test orders (not results) from two large commercial laboratory companies.
ELR is the automated messaging of notifiable diseases from laboratory information management systems directly to a health department's reporting system (Wurtz and Cameron, 2005). A small number of health departments have developed ELR. States, such as Florida (Florida Department of Health, 2002), Hawaii (Effler et al., 1999), Oregon (Oregon Department of Human Services, n.d.), Pennsylvania (Pennsylvania Department of Health, 2005), and Texas (Texas Department of State Health Services, 2004) have established or have began testing ELR systems.
The Laboratory Response Network (LRN) was established by the CDC and became operational in 1999. The LRN maintains a laboratory network of health department, military, and international laboratories that can respond to bioterrorism, chemical terrorism, and other public health emergencies (CDC, 2005e). At present, 96 state and local public health laboratories constitute the 146 laboratories that are members of the LRN. Of these, 62 state, territorial, and metropolitan public health laboratories are part of the LRN's chemical testing network.
The CDC maintains a Web site that provides secure access for more than 1,000 LRN laboratory workers (CDC, 2005e). We further discuss the LRN and provide a map of LRN locations in Chapter 8.
Although PHIN is not one of the four types of information systems just listed, we discuss it because it is the current incarnation of standards and specification work that began in NEDSS. Note that the word network in PHIN connotes not just an electronic network, such as the Internet, but software and computer systems needed to achieve a vision of more than 3,000 health departments in the United States functioning as a single virtual system. PHIN is a CDC-led initiative. PHIN creates specifications for data elements and promulgates or develops standards for exchange of data related to disease, investigations, and response. The PHIN specifications cover five functional areas: (1) detection and monitoring, which includes specifications for disease and risk surveillance and national health status indicators; (2) data analysis, which includes specifications to facilitate real-time evaluation of data; (3) knowledge management, which are specifications intended to facilitate access to reference materials and integrated distance learning materials; (4) alerting and communications, which are specifications to enable emergency alerting, routine professional discussions, and collaborative activities; and (5) response, which includes specifications that support the distribution of recommendations, and the administration of antibiotic prophylaxis and vaccinations (CDC, 2005b).
In conjunction with the PHIN standards, the CDC is beginning to develop software, such as the PHIN Messaging System (MS), which is a system used for secure message transport compatible with PHIN standards (CDC, 2005c).
7.4. Other Systems and Initiatives
Health departments have relied on vital records as summative measures of the health of a population for two centuries. The current vital records system is labor intensive, requiring manual data entry, and is vulnerable to human error. The current system also entails time lags, which are not a limitation for the traditional uses of the data (estimating life expectancy and the other uses mentioned) but reduce their value for biosurveillance. Electronic registration of vital records may also reduce errors.
Electronic filing of death certificates is uncommon. In 1998, the City of New York commissioned the development and implementation of the New York Department of Health and Mental Hygiene's Electronic Death Registration System (EDRS). Although it was estimated that the system would be ready for full implementation by December 1999, it was not functioning as of 2003. In 2005, the new system entered a pilot phase (City of New York, 2003).
The Health Alert Network (HAN) is a CDC project that funds state and local health departments to improve their network infrastructure. The functional goal of the project is to create the capacity to broadcast and receive health alerts. A health alert is a notice that is issued to inform medical personnel about a certain urgent medical matter that requires their immediate awareness. The capability to send health alerts to front-line clinicians and laboratories is also part of the scope of HAN (Committee on Health Promotion and Disease Prevention, 2003).
Figure 5.7 provides two examples of a HAN alert, one issued by the CDC and one issued by the Pennsylvania Department of Health.
Under the Secure Data Network (SDN) project, the CDC promulgates standards and specifications for maintaining the confidentiality of data and providing a secure method for encrypting and transferring files from state health departments to a CDC program application via the Internet (CDC, 2002). The CDC Biosense system (Loonsk, 2004) uses such a network.
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