Contextual Variation of Clinical Notes induced by EHR Migration.

Area of Science:

• Medical Informatics
• Natural Language Processing
• Clinical Documentation

Background:

• Clinical notes exhibit substantial structural and semantic variability across Electronic Health Record (EHR) systems, institutions, and geographical sites.
• This heterogeneity poses a significant challenge for the development and deployment of portable Natural Language Processing (NLP) models used in clinical research and practice.
• Understanding the sources of this variation is crucial for improving the reliability and generalizability of NLP applications in healthcare.

Purpose of the Study:

• To quantify the semantic and syntactic variations in clinical notes.
• To assess the impact of Electronic Health Record (EHR) system context and medical specialty on clinical note language.
• To evaluate the influence of spatial context (different sites) on clinical note similarity.

Main Methods:

• Comparative analysis of clinical notes from physicians across four medical specialties.
• Measurement of semantic and syntactic similarity between notes from different Electronic Health Record (EHR) systems and sites.
• Utilized NLP techniques to assess language variation in a multi-site, multi-specialty cohort.

Main Results:

• Significant semantic and syntactic variations were identified, primarily driven by the Electronic Health Record (EHR) system context and differences between medical specialties.
• Variation attributed to spatial context across different sites was found to be minor.
• The study highlights that the EHR system and medical specialty are key determinants of clinical note language variation.

Conclusions:

• Clinical language models must be adapted to account for process differences at the sublanguage level specific to each medical specialty.
• Generalizability of NLP models across different clinical settings and specialties requires addressing specialty-specific linguistic patterns.
• Future NLP model development should focus on specialty-level adaptation to overcome heterogeneity in clinical documentation.