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Language Models for Standardising Clinical Notes and Information Extraction in Addiction Psychiatry-An Empirical

Haritha Gireesh1, Lekhansh Shukla2, Prakrithi Shivaprakash2

  • 1Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India.

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|October 29, 2025
PubMed
Summary

Fine-tuned Large Language Models (LLMs) improve addiction psychiatry clinical notes by proofreading and extracting substance use data. This enhances documentation readability and facilitates research, even with limited computational resources.

Keywords:
deep learningelectronic health recordsgenerative artificial intelligencenatural language processing addiction

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Psychiatry
  • Health Informatics

Background:

  • Electronic health records (EHRs) contain unstructured clinical notes vital for addiction psychiatry.
  • Clinical notes often have errors, necessitating proofreading for accuracy and readability.
  • NLP methods and Large Language Models (LLMs) can be adapted for clinical note improvement and data extraction.

Purpose of the Study:

  • To evaluate NLP methods for proofreading clinical notes.
  • To adapt LLMs for proofreading and extracting substance-related information from EHRs.
  • To compare the performance of adapted LLMs against existing solutions and human performance.

Main Methods:

  • Analysis of 6500 clinical notes from a 5-year addiction medicine EHR dataset (2018-2023).
  • Proofreading involved correcting spelling and expanding abbreviations; information extraction focused on substance use and time since last use.
  • Comparison of a fine-tuned LLAMA-3.2-3b model against Generative Pretrained Transformer-4-o and human proofreading via a masked preference experiment.

Main Results:

  • LLM-based proofreading significantly improved note readability and reduced out-of-vocabulary words.
  • The fine-tuned LLAMA model outperformed Generative Pretrained Transformer-4-o in both proofreading and information extraction.
  • Human evaluators preferred model-corrected notes over human-corrected versions in 62% of trials (p < 0.001).
  • Information extraction achieved high overall performance (Mean F1 0.99) but struggled with rarer substance classes.

Conclusions:

  • Fine-tuned LLMs effectively standardize clinical notes and extract structured data in addiction psychiatry.
  • These capabilities enhance clinical documentation readability, interdisciplinary communication, and research cohort creation.
  • Automated information extraction can reduce staff burden and improve patient outcomes by flagging critical data like 'time since last drink'.
  • Open-source LLMs can be adapted for specialized tasks using limited computational resources, ensuring data privacy and security on consumer-grade servers.