Towards LLM-Guided Healthcare Dataset Harmonization

Electronic health record (EHR) datasets come in various schemas and can contain a range of data types, measurement units, and variables that share duplicate semantic content. The process of bringing such datasets into a common schema with consistent values, so that it is possible to perform queries uniformly, is known as harmonization. However, performing this process manually can be both time-consuming and prone to errors. In this work, we present a web-based platform that semi-Automates the harmonization and linking of EHR datasets through a human-in-The-loop framework, guiding users with the use of large language models (LLMs). Our solution is a two-stage harmonization pipeline that keeps schema metadata processing online while handling patient-level data locally, to align with HIPAA data privacy principles. In the first stage, users harmonize and link only non-identifiable schema information. In the second stage, sensitive value-level harmonization occurs entirely on the user's system, so no private and protected health information ever leaves their environment. Throughout both stages, we expect that LLM-powered suggestions could potentially speed up the harmonization and linking processes.