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Research

Evaluating ChatGPT for converting clinic letters into patient-friendly language: a quantitative study

Simon C Cork and Keith Hopcroft
BJGP Open 2025; 9 (3): BJGPO.2024.0300. DOI: https://doi.org/10.3399/BJGPO.2024.0300
Simon C Cork
1 School of Medicine, Anglia Ruskin University, Bishop Hall Road, Chelmsford, UK
PhD
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  • ORCID record for Simon C Cork
  • For correspondence: simon.cork{at}aru.ac.uk
Keith Hopcroft
2 Laindon Medical Group, Basildon, UK
MBBS
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  • Objective readability scores Comparison of readability between original clinic letters and AI-generated outputs revealed no significant difference in A: Flesch-Kincaid score, B: SMOG score, C: Coleman-Liau score or D: Automatic readability score. A significant decrease in readability score of AI generated output (equivalent to a decrease in reading age) was observed in E: Gunning Fog score (P
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    Figure 1. Objective readability scores

    Comparison of readability between original clinic letters and AI-generated outputs revealed no significant difference in A: Flesch-Kincaid score, B: SMOG score, C: Coleman-Liau score or D: Automatic readability score. A significant decrease in readability score of AI generated output (equivalent to a decrease in reading age) was observed in E: Gunning Fog score (P<0.05) compared with the original clinic letters. * P<0.05.

  • Results from patient representative analysis Patient representatives were presented with a mixture of original clinic letters and AI-generated outputs (but not the original and AI output of the same letter) and asked to rate their understanding of the content across a series of questions. There was a significant increase in understanding of content and approval of AI-generated outputs compared with original clinic letters. There was a significant decrease in the requirement for patient representatives to require medical assistance with understanding the content of AI-generated outputs compared with original clinic letters. ****P
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    Figure 2. Results from patient representative analysis

    Patient representatives were presented with a mixture of original clinic letters and AI-generated outputs (but not the original and AI output of the same letter) and asked to rate their understanding of the content across a series of questions. There was a significant increase in understanding of content and approval of AI-generated outputs compared with original clinic letters. There was a significant decrease in the requirement for patient representatives to require medical assistance with understanding the content of AI-generated outputs compared with original clinic letters. ****P<0.0001

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    Table 1. Demographics of patient representatives
    Demographic criteriaPatient representatives
    Gender: female (n, %)7, 47%
    Age range, years (n)35–44 = 1
    45–54 = 1
    55–64 = 4
    ≥65 = 9
    Education (n)Secondary education = 3
    Further education = 4
    Degree or higher = 8
    Ethnicity (n)White/Caucasian = 13
    Mixed/multiple ethnic groups = 2
    English as first language: Yes (n, %)15, 100%
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Evaluating ChatGPT for converting clinic letters into patient-friendly language: a quantitative study
Simon C Cork, Keith Hopcroft
BJGP Open 2025; 9 (3): BJGPO.2024.0300. DOI: 10.3399/BJGPO.2024.0300

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Evaluating ChatGPT for converting clinic letters into patient-friendly language: a quantitative study
Simon C Cork, Keith Hopcroft
BJGP Open 2025; 9 (3): BJGPO.2024.0300. DOI: 10.3399/BJGPO.2024.0300
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Keywords

  • Family practice
  • Hospital referrals
  • patient perspectives
  • Primary healthcare
  • General practitioners
  • artificial intelligence
  • Generative AI

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