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Large Language Models for Clinical Note Simplification: A Systematic Review and Experimental Evaluation of Medical Text Readability.

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Title: Large Language Models for Clinical Note Simplification: A Systematic Review and Experimental Evaluation of Medical Text Readability.
Authors: Teichmann M; Brandenburg University of Applied Science, Brandenburg a. d. Havel, Germany., Özkara Menekseoglu P; Department of Psychiatry and Psychotherapy, Immanuel Klinik Rüdersdorf, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany., Schwarz J; Department of Psychiatry and Psychotherapy, Immanuel Klinik Rüdersdorf, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany., Karbach U; Institute of Medical Sociology, Health Services Research, and Rehabilitation Science (IMVR), Cologne, Germany., Purohit AM; Brandenburg University of Applied Science, Brandenburg a. d. Havel, Germany.
Source: Studies in health technology and informatics [Stud Health Technol Inform] 2026 Sep 17; Vol. 340, pp. 148-162.
Publication Type: Journal Article; Systematic Review
Language: English
Journal Info: Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991-
MeSH Terms: Large Language Models* , Comprehension* , Electronic Health Records* , Natural Language Processing*, Humans ; Germany
Abstract: Since the introduction of the Patient Rights Act, patients in Germany have gained legal access to their medical records, including clinical notes. However, these documents are typically written for healthcare professionals and are often difficult for patients to understand due to specialized terminology, abbreviations, and complex sentence structures. Large Language Models (LLMs) offer new opportunities to automatically simplify such texts while preserving medically relevant information. This study investigates the potential of LLMs to improve the readability of German clinical notes by combining a systematic literature review with an experimental evaluation. Ten freely available LLMs were assessed using five synthetic clinical notes, which were simplified through standardized prompts designed to ensure linguistic clarity while maintaining content fidelity. Readability was analyzed using established indices (Flesch Reading Ease, Wiener Sachtextformel, LIX, SMOG, and Coleman-Liau) and complemented by a novel analysis of medical terminology and abbreviation density as indicators of domain-specific complexity. The results show that all LLMs substantially increased overall text length while consistently reducing the density of technical terms and abbreviations. However, no model achieved consistent improvements across all readability indices, highlighting limitations of traditional metrics in the medical domain. Models such as Mistral, ChatGPT, and Copilot demonstrated the highest efficiency in balancing linguistic simplification and text length. Overall, LLMs show strong potential to enhance the accessibility of clinical documentation for patients. However, their effectiveness depends on model selection, prompt design, and evaluation methodology. The findings suggest that conventional readability metrics should be extended with domain-specific measures to more accurately assess comprehensibility in medical texts.
Contributed Indexing: Keywords: Artificial Intelligence; Health Literacy; Medical Records; Natural Language Processing; Patient Empowerment
Entry Date(s): Date Created: 20260917 Date Completed: 20260917 Latest Revision: 20260917
Update Code: 20260918
DOI: 10.3233/SHTI260997
PMID: 42752479
Database: MEDLINE
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  Data: Large Language Models for Clinical Note Simplification: A Systematic Review and Experimental Evaluation of Medical Text Readability.
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  Data: <searchLink fieldCode="AU" term="%22Teichmann+M%22">Teichmann M</searchLink>; Brandenburg University of Applied Science, Brandenburg a. d. Havel, Germany.<br /><searchLink fieldCode="AU" term="%22Özkara+Menekseoglu+P%22">Özkara Menekseoglu P</searchLink>; Department of Psychiatry and Psychotherapy, Immanuel Klinik Rüdersdorf, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany.<br /><searchLink fieldCode="AU" term="%22Schwarz+J%22">Schwarz J</searchLink>; Department of Psychiatry and Psychotherapy, Immanuel Klinik Rüdersdorf, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany.<br /><searchLink fieldCode="AU" term="%22Karbach+U%22">Karbach U</searchLink>; Institute of Medical Sociology, Health Services Research, and Rehabilitation Science (IMVR), Cologne, Germany.<br /><searchLink fieldCode="AU" term="%22Purohit+AM%22">Purohit AM</searchLink>; Brandenburg University of Applied Science, Brandenburg a. d. Havel, Germany.
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  Data: <searchLink fieldCode="JN" term="%229214582%22">Studies in health technology and informatics</searchLink> [Stud Health Technol Inform] 2026 Sep 17; Vol. 340, pp. 148-162.
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  Data: Journal Article; Systematic Review
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22IOS+Press%22">IOS Press </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>9214582 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-8365 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209269630%22">09269630 </searchLink><i>NLM ISO Abbreviation: </i>Stud Health Technol Inform <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: Amsterdam ; Washington, DC : IOS Press, 1991-
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  Data: Since the introduction of the Patient Rights Act, patients in Germany have gained legal access to their medical records, including clinical notes. However, these documents are typically written for healthcare professionals and are often difficult for patients to understand due to specialized terminology, abbreviations, and complex sentence structures. Large Language Models (LLMs) offer new opportunities to automatically simplify such texts while preserving medically relevant information. This study investigates the potential of LLMs to improve the readability of German clinical notes by combining a systematic literature review with an experimental evaluation. Ten freely available LLMs were assessed using five synthetic clinical notes, which were simplified through standardized prompts designed to ensure linguistic clarity while maintaining content fidelity. Readability was analyzed using established indices (Flesch Reading Ease, Wiener Sachtextformel, LIX, SMOG, and Coleman-Liau) and complemented by a novel analysis of medical terminology and abbreviation density as indicators of domain-specific complexity. The results show that all LLMs substantially increased overall text length while consistently reducing the density of technical terms and abbreviations. However, no model achieved consistent improvements across all readability indices, highlighting limitations of traditional metrics in the medical domain. Models such as Mistral, ChatGPT, and Copilot demonstrated the highest efficiency in balancing linguistic simplification and text length. Overall, LLMs show strong potential to enhance the accessibility of clinical documentation for patients. However, their effectiveness depends on model selection, prompt design, and evaluation methodology. The findings suggest that conventional readability metrics should be extended with domain-specific measures to more accurately assess comprehensibility in medical texts.
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  Data: <i>Keywords: </i>Artificial Intelligence; Health Literacy; Medical Records; Natural Language Processing; Patient Empowerment
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