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\begin{document}

\title{The use of a large language models to create plain language summaries of
evidence reviews in healthcare: a feasibility study}



\author[1]{Colleen Ovelman}%
\author[2]{Shannon Kugley}%
\author[2]{Gerald Gartlehner}%
\author[2]{Meera Viswanathan}%
\affil[1]{Cochrane}%
\affil[2]{RTI International}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
Introduction: Plain language summaries (PLSs) make complex healthcare
evidence accessible to patients and the public. Large language models
(LLMs) may assist in generating accurate, readable PLSs. This study
explored using the LLM Claude 2 to create PLSs of evidence reviews from
the Agency for Healthcare Research and Quality (AHRQ) Effective Health
Care Program. Methods: We selected 10 evidence reviews published from
2021-2023, representing a range of methods and topics. We iteratively
developed a prompt to guide Claude 2 in creating PLSs which included
specifications for plain language, reading level, length, organizational
structure, active voice, and inclusive language. PLSs were assessed for
adherence to prompt specifications, comprehensiveness, accuracy,
readability, and cultural sensitivity. Results: All PLSs met the word
count. We judged one PLS as fully comprehensive; 7 mostly comprehensive.
We judged 2 PLSs as fully capturing the PICO elements; 5 with minor PICO
errors. We judged 3 PLSs as accurately reporting the results; 4 with
minor result errors. We judged 3 PLSs as having major result errors for
incorrectly reporting total participants. Five PLSs met the target
6th-8th grade reading level. Passive voice use averaged 16\%. All PLSs
used inclusive language. Conclusions: LLMs show promise for assisting in
PLS creation but likely require human input to ensure accuracy,
comprehensiveness, and the appropriate nuances of interpretation.
Iterative prompt refinement may improve results and address the needs of
specific reviews and audiences. As text-only summaries, the AI-generated
PLSs could not meet all consumer communication criteria, such as textual
design and visual representations. Further testing should explore how to
best leverage LLM support in drafting PLS text for complex evidence
reviews.%
\end{abstract}%



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