Table 2. Frequency of field completion for patient demographic and care-provider information from initial reports Traditional reporting (%) Electronic reporting (%) Facility 93 97 Address 32 79a Phone 29 35 Gender 94 99a Race 38 72a Age 92 98 Date of birth 38 95a aSignificant change as determined by McNemar’s test (p<0.05). pathogens, conditions that are often underreported (3,5,15). The underreported cases originated from all of the organizations participating in the electronic reporting system (data not shown). Review of a subset of underre- ported cases suggested three common root causes: misin- terpretation of jurisdictional guidelines, misunderstanding of reportable specimen criteria, and use of outdated report- ing guidelines by healthcare providers (failure to report pathogens recently added to the guidelines, as occurred with the underreported giardiasis cases). The data clearinghouse approach to biosurveillance and disease reporting also offers opportunities to relieve healthcare providers from managing multiple jurisdiction- al relationships, which can lead to underreporting. Through electronic reporting, data were reported from patients residing in Kansas whose laboratory work was performed in Kansas City to both the Department of Health and Environment (with jurisdiction based on the state of residence of the patient) and KCHD (with jurisdic- tion over the performing laboratory). Reducing underre- porting is a critical step in building a threshold-based auto- mated alerting infrastructure because the baseline data gathered by an electronic reporting system is more accu- rate than that gathered by traditional methods. During the early stages of this work, we identified a number of confounding issues. For example, a laboratory technician entered a report with the word “No” on one line, followed by “Bordetella pertussis” on the next line, with the intent of negating the positive result. This type of data entry error, which led to a few false-positive results propa- gating into the clearinghouse during the first few weeks of operation, has also been an issue for other electronic report- RESEARCH ing systems (8). After recognizing this issue, we added con- text-sensitive logic to clearly flag a reportable isolate pre- ceded by “No” or “Not” as a potential false-positive. We evaluated whether test orders were predictive of disease incidence. However, in the absence of major out- breaks during the period evaluated, our data were incon- clusive. Surveillance of laboratory test orders (i.e., ova and parasite procedure orders) would be useful as an early warning of a water-supply contamination crises, such as that experienced by Milwaukee in 1993 when cryp- tosporidium contaminated the city water supply and caused >403,000 illnesses (16). Most patients whose results were evaluated by the sys- tem resided within approximately 150 km of the Kansas City metropolitan area; however, patients from eastern Missouri, western Kansas, and other U.S. states also were evaluated by the system. The area covered by the clearing- house crosses the state line that transects the Kansas City metropolitan area. Plans for a national public health infra- structure (e.g., the National Electronic Disease Surveillance System [NEDSS] [17]) rely on a process in which patient results are first sent to the state health departments and then forwarded to the Centers for Disease Control and Prevention, currently with identifiable infor- mation removed. Reconciling duplicate cases without such information will prove difficult. Systems such as NEDSS that manage data based on politically defined boundaries have inherent inefficiencies that could be rectified by direct reporting to a data clearinghouse. A data clearing- house can apply jurisdictional filters that control the distri- bution of reports, while also offering the opportunity to perform rapid analysis of trends across politically defined boundaries. We considered issues that have proven to be challeng- ing for other electronic reporting projects. In particular, the use of nonstandardized data, subject to errors in either database design or during data entry, created challenges. Overall, we found that the data clearinghouse approach to biosurveillance offers many benefits, including ease and speed of implementation, improved timeliness and com- pleteness of data, efficiency of scale from a central data- mapping operation, and the ability to deliver data to multi- ple jurisdictions. Figure 4. Number of patient encounters evalu- ated by the data clearing- house system for poten- tial reportable events per zip code. Emerging Infectious Diseases • Vol. 9, No. 10, October 2003 1285