Speaking the Same Language: Leveraging LLMs in Standardizing Clinical Data for AI

TL;DR

This paper explores using large language models to standardize clinical healthcare data, reducing manual effort and improving data quality for AI applications in healthcare.

Contribution

It introduces a novel approach leveraging LLMs to map clinical data schemas to standard attributes, enhancing data standardization efficiency.

Findings

LLMs significantly reduce manual data curation efforts

The methodology improves data standardization accuracy

It accelerates AI integration in healthcare systems

Abstract

The implementation of Artificial Intelligence (AI) in the healthcare industry has garnered considerable attention, attributable to its prospective enhancement of clinical outcomes, expansion of access to superior healthcare, cost reduction, and elevation of patient satisfaction. Nevertheless, the primary hurdle that persists is related to the quality of accessible multi-modal healthcare data in conjunction with the evolution of AI methodologies. This study delves into the adoption of large language models to address specific challenges, specifically, the standardization of healthcare data. We advocate the use of these models to identify and map clinical data schemas to established data standard attributes, such as the Fast Healthcare Interoperability Resources. Our results illustrate that employing large language models significantly diminishes the necessity for manual data curation and elevates the efficacy of the data standardization process. Consequently, the proposed methodology has the propensity to expedite the integration of AI in healthcare, ameliorate the quality of patient care, whilst minimizing the time and financial resources necessary for the preparation of data for AI.