Laboratory data collected in the electronic health record as part of routine care can be used in secondary research. For example, the US Department of Veterans Affairs maintains a data warehouse covering over 20 million individuals and 6.6 billion lab tests. However, data aggregation in such a data warehouse can be difficult. In order to retrieve all or nearly all of one type of lab result with a high degree of precision, we perform clinical concept adjudication, which is the process of an expert determining which database records correspond to a target clinical concept. In this work, we develop an interactive machine learning tool to "extend the reach" of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in order to lower barriers to collaboration, increase transparency of adjudication, and to promote efficiencies and data reuse.
Keywords: Clinical Laboratory Information Systems; Supervised Machine Learning; Systems Integration.