Abstract
Background/Aims Assessment of data incorporated into the Virtual Data Warehouse (VDW) is crucially important when building a new content area such as laboratory results. At KPCO, we identified two main challenges while constructing tables of laboratory results for the VDW. The first was ensuring that we constructed clinically meaningful laboratory test types. The second was confirming that we identified and classified the correct set of records from source data repositories. The aim of our work was therefore to ensure that these two challenges were successfully met
Methods Initial priority tests were broadly categorized as chemistry, hematology, microbiology, or challenge tests. Many laboratory results could be characterized into a single test type. Other laboratory results required one or more of the following characteristics to differentiate subtypes: assay method; test subtypes such as isoenzymes; result type; fasting status; specimen site; and challenge dose and time since dose. Laboratory results were extracted from two data repositories. The first database includes records from 1999–2004. The second database stores records since 2005 with partial backfill of records from 2004 and earlier. Variable names and values did not follow a common naming convention between the two data sources. Extraction and classification rules were based on Logical Observation Identifiers Names and Codes (LOINCs), test names, result units, and Current Procedural Terminology (CPT) codes.
Results We identified numerous issues while developing the VDW laboratory content area: incomplete clinical information; distinct laboratory types with similar names; missing, incorrect, obsolete, or site-specific component codes; incorrect sample collection times; changes in source data streams; clinical practices that differ from guidelines; and little background knowledge of laboratory tests among those processing the data. Each challenge and the approach to addressing each issue will be illustrated with an example.
Conclusions A systematic approach to collection and processing of laboratory results can yield high-quality, clinically useful data. Key elements of this systematic approach include a logical and flexible taxonomy of laboratory types and careful consideration of identifier codes and supplemental information to categorize laboratory results. Developers should expect a variety of deviations in the data from expected patterns.
