Clinical Laboratory Data for Biosurveillance
This chapter provides general background on the types of data used for biosurveillance, syndromic surveillance and traditional public health surveillance, with particular emphasis on the use of clinical laboratory data. Overviews of types of surveillance,
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CHAPTER OVERVIEW This chapter provides general background on the types of data used for biosurveillance, syndromic surveillance and traditional public health surveillance, with particular emphasis on the use of clinical laboratory data. Overviews of types of surveillance, the history of clinical laboratory testing and different types of laboratories are provided as background. A more detailed discussion of the roles and characteristics of clinical laboratory data with respect to biosurveillance includes comments on data standards and analytic requirements, with particular emphasis on understanding possible sources of variability or artifacts during data analysis and interpretation. Keywords: Biosurveillance; Electronic laboratory reporting; Laboratory data; Public health surveillance; Syndromic surveillance
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INTRODUCTION
Biosurveillance may be viewed as an extension of public health surveillance. The term biosurveillance is typically defined as the process of monitoring selected aspects of the health of a population in such a way as to facilitate early detection and monitoring of an event of public health concern, as well as supporting situational awareness and response to the event. In terms of data, biosurveillance utilizes a broader range of data types and sources than traditional public health surveillance.
D. Zeng et al. (eds.), Infectious Disease Informatics and Biosurveillance, Integrated Series in Information Systems 27, DOI 10.1007/978-1-4419-6892-0_4, © Springer Science+Business Media, LLC 2011
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From a historical perspective, there have been dramatic changes in the volume and types of data available for public health surveillance since the sixteenth century when the introduction of the London Bills of Mortality [1] represented an early effort to systematically collect data on causes of death. Since that time, the scope, types, quantities and level of detail of the data collected have continued to expand. The availability of data and the ability to process it efficiently have progressed, of necessity, along parallel paths, particularly since the introduction of computers into healthcare. The earliest applications of computing to healthcare began in the 1950s, escalating with the development of MUMPS (Massachusetts General Hospital Utility MultiProgramming System) in 1966 and continuing to expand with the emergence of specialized medical computing professional societies [2]. The pace of these developments has escalated dramatically as computing power in general, and advances in health information technology (HIT) in particular, have continued to accelerate. This has, in turn, enabled both access to new data streams as well as supporting new, and increasingly complex, analytic technologies. As a result, a variety of new approaches to surveillance have been developed, each of which may take advantage of – and be best served by – different types of data and detection algorithms. Clinical laboratory data represents a critical information component of data used for biosurveillance in mu
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