Efficient Standardization of Clinical Notes using Large Language Models

TL;DR

This paper introduces a large language model-based method to standardize clinical notes, improving their consistency, readability, and readiness for data extraction and interoperability in healthcare systems.

Contribution

The study presents a novel LLM approach for comprehensive clinical note standardization, addressing grammatical, spelling, terminology, abbreviations, and formatting inconsistencies.

Findings

Corrected an average of 4.9 grammatical errors per note

Expanded 15.8 abbreviations and acronyms per note

No significant data loss observed after standardization

Abstract

Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement, population health, precision medicine, decision support, and research. We present a large language model approach to standardizing a corpus of 1,618 clinical notes. Standardization corrected an average of $4.9 +/- 1.8$ grammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$ non-standard terms to standard terminology, and expanded $15.8 +/- 9.1$ abbreviations and acronyms per note. Additionally, notes were re-organized into canonical sections with standardized headings. This process prepared notes for key concept extraction, mapping to medical ontologies, and conversion to interoperable data formats such as FHIR. Expert review of randomly sampled notes found no significant data loss after standardization. This proof-of-concept study demonstrates that standardization of clinical notes can improve their readability, consistency, and usability, while also facilitating their conversion into interoperable data formats.