Using ChatGPT to write patient clinic letters
Open accessAbstract
The appropriate recording and communication of clinical information between clinicians and patients are of paramount importance. Recently there has been a much-needed drive to improve the information that is shared with patients.1 However, the preparation of clinical letters can be time consuming. Although there has been an increase in the use of letter templates and voice recognition systems, with the aim of improving efficiency, novel technologies such as natural language processing (NLP) and artificial intelligence (AI) have the power to revolutionise this area of practice.
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LLMs, with advanced natural language processing abilities, offer a more comprehensive understanding of the medical literature, patient data, and individualized care requirements.
“LLMs, with advanced natural language processing abilities, offer a more comprehensive understanding of the medical literature, patient data, and individualized care requirements.146,147”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [146]
In “Using ChatGPT to write patient clinic letters” (page not provided), the paper describes NLP’s ability to process language and tests ChatGPT letters generated from limited clinical input, including instructions involving national guidelines. It does not establish that LLMs have a more comprehensive understanding of medical literature, patient data, or individualized care requirements. (LLM, conf=0.82)
Experimental — accuracy not yet measuredBoth Chat GPT and ambient AI have shown promise in streamlining clinical documentation in a variety of settings.
“Both Chat GPT and ambient AI have shown promise in streamlining clinical documentation in a variety of settings [5–7].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [7]
Generation of inaccurate content can have severe negative consequences in health care.
“Additionally, the generation of inaccurate content can have severe negative consequences in health care; therefore, this valid concern should be cautiously considered in health care practice [30,32,53,84].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [84]
LLMs are built using natural language processing techniques and designed to recognize and understand the structure and meaning of human language, classify texts according to their content or purpose, and generate responses that are appropriate and coherent.
“LLMs are built using natural language processing techniques and designed to recognize and understand the structure and meaning of human language, classify texts according to their content or purpose, and generate responses that are appropriate and coherent.146”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [146]
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“Additionally, the generation of inaccurate content can have severe negative consequences in health care; therefore, this valid concern should be cautiously considered in health care practice [30,32,53,84].”
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“The use of ChatGPT and similar LLMs for the generation of patient friendly letters is something that many others in the healthcare space have been experimenting with [5][10][23][24][25].”
Method
- Authors
- Ali, S. R., Dobbs, T. D., Hutchings, H. A., & Whitaker, I. S.
- Affiliations
- Morriston Hospital (GB)
- Swansea University (GB)
- Corresponding author
- Ali, Stephen R
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 5
- Issue
- 4
- Pages
- e179-e181
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4323350039
- Cited by
- 375
- References
- 1
- Citations by year
- 2026: 38 · 2025: 92 · 2024: 143 · 2023: 101
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Health and Care Research Wales
- Scar Free Foundation
- BAPRAS
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- ChatGPT, artificial intelligence in healthcare, natural language processing, letter templates, voice recognition, patient communication
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