A Language-Modeling Approach to Health Data Interoperability

The need for health providers to share information is a pressing need in our ever more connected world. A patient's health information should seamlessly flow from labs to hospitals to primary care offices. To address this need, in this paper we present the Health E-Match, which focuses on the matching health terms in support of semantic interoperability. Health E-Match determines the semantic similarity between data items, realizing, for instance, that "BHGC (UR)" and "BETA-HCG (QUAL)" both refer to the same pregnancy test, known as "Beta human chorionic gonadotropin, urine qualitative." Our approach is grounded in probabilistic machine learning, and leverages several sophisticated methods for comparing the similarity between medical data items beyond simple edit distance. We present two large scale, real-world experiments to verify that our approach is both accurate and has the ability to eventually be "universal" in that models trained on one set of data translate to strong performance on data from a completely different provider.