New Technology and Implications for Healthcare and Public Health: The Case of Probabilistic Record Linkage

New Technology and Implications for Healthcare and Public Health: The Case of Probabilistic Record Linkage

Gulzar H. Shah (National Association of County and City Health Officials, USA), Kaveepan Lertwachara (California Polytechnic State University, USA) and Anteneh Ayanso (Brock University, Canada)
DOI: 10.4018/978-1-61520-733-6.ch012
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Abstract

In this chapter, the authors provide a review of recent developments in probabilistic record linkage and their implications in healthcare research and public health policies. Their primary objective is to pique the interest of researchers and practitioners in the healthcare and public health communities to take full advantage of record linkage technologies in completing a health care scenario where different pieces of patient records are collected and managed by different agencies. A brief overview of probabilistic record linkage, software available for such record linkage, and type of functions provided by probabilistic record linkage software is provided. Specific cases where probabilistic linkage has been used to bridge information gaps in informing public health policy and enhancing decision-making in healthcare delivery are described in this chapter. Issues and challenges of integrating medical records across distributed databases are also outlined, including technical considerations as well as concerns about patient privacy and confidentiality.
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Overview Of Record Linkage Concepts And Techniques

Record linkage is a computer-based process to match records from different and often heterogeneous sources of data that refer to the same entities such as persons, events, or other objects of interest. However record linkage is sometimes performed within a single data set when multiple records are present in a single database for a person or other entity (e.g., records for multiple hospitalizations in a hospital discharge data set for a 12-month period). Record linkage within a single data set is also performed to remove duplicate records, referred to as “deduplication” (Winkler, 1999).

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