  Objective: To design and evaluate a general framework for interactive record
linkage using a convenient algorithm combined with tractable Human Intelligent
Tasks (HITs; i.e. micro tasks requiring human judgment) that can support
reproducible data science. Materials and Methods: Accurate linkage of real data
requires both automatic processing of well-defined tasks and human processing
of tasks that require human judgment (i.e., HITs) on messy data. We present a
reproducible, interactive, and iterative framework for record linkage called
VIEW (Visual Interactive Entity-resolution Workbench). We implemented and
evaluated VIEW by integrating two commonly used hospital databases, the
American Hospital Association (AHA) Annual Survey of Hospitals and the Medicare
Cost Reports for Hospitals from CMS. Results: Using VIEW to iteratively
standardize and clean the data, we linked all Texas hospitals common in both
databases with 100% precision by confirming 78 approximate linkages using HITs
and manually linking 28 hospitals using HITs. Discussion: Similarities in
hospital names and addresses and the dynamic nature of hospital attributes over
time make it impossible to build a fully automated linkage system for hospitals
that can be maintained over time. VIEW is a software that supports a
reproducible semi-automated process that can generate and track HITs to be
reviewed and linked manually for messy data elements such as hospitals that
have been merged. Conclusion: Effective software that can support the
interactive and iterative process of record linkage, and well-designed HITs can
streamline the linkage processes to support high quality replicable research
using messy real data.
