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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Outpatient clinic letters are a cornerstone of patient communication, yet their readability and accessibility remain poorly addressed. Guidance from the Academy of Medical Royal Colleges (AoMRC) and NHS England recommends writing letters directly to patients. The benefits are compelling: improved comprehension, greater engagement, enhanced trust in healthcare professionals, and potential system-wide efficiencies. However, concerns about adherence and complexity persist, limiting patient understanding and shared decision-making. Physician training and artificial intelligence (AI) tools offer opportunities to improve readability, though limited research shows mixed results. This literature review synthesises evidence on outpatient clinic letter readability, adherence to AoMRC recommendations, and trials evaluating interventions to simplify clinic letters.
A PubMed (Medline) search was conducted on 16/1/2025 using terms such as “clinic letter”, “readability”, and related synonyms. Reference lists and works by the same authors were also reviewed.
A significant proportion of research investigating clinic letter readability has been conducted in the UK. Although no UK-specific readability guidance exists, studies across specialties report levels exceeding the Grade 6 (reading age 11–12) threshold recommended by the U.S. Department of Health and Human Services. Resident doctors write significantly more complex letters than consultants.
Education-based interventions to improve readability have shown limited impact. AI models such as ChatGPT have also struggled to improve readability scores. While AI-authored letters demonstrate moderate clinical accuracy and “humanness,” they can include embellishments or less tactful phrasing for sensitive issues. Notably, ChatGPT v4.0 improved readability scores significantly compared to v3.5 (p<0.001), especially when simplifying complex letters by resident doctors.
Writing letters directly to patients (as per AoMRC recommendations) significantly reduces terms patients can’t understand (P<0.001). This approach aligns with patient preferences, with reported preference rates ranging from 52.5–100%. (13–15) In one study, 56 GPs cited benefits such as increased patient satisfaction (85.7%) and improved compliance (82%), rating these letters as equally or more useful than GP-directed ones (83.6%).
However, only 20–48% of GPs preferred receiving solely patient-directed letters. Concerns included missing information and preference for traditional GP letter structure. Paternalism persists: 20% of GPs underestimated patient comprehension, and 21% felt patients should not receive clinic letters.
Despite patient preference, adherence to AoMRC recommendations remains low—only 0–7% of surveyed letters were patient-directed. Allied health professionals showed higher adherence. Barriers included time constraints and unawareness of AoMRC guidance. Yet, all patient-directed letters remained above U.S. readability recommendations.
Outpatient clinic letters are consistently above the general population’s reading level. Shifting to patient-directed letters aligns with preferences and may improve readability; however, resistance exists among GPs. Education initiatives and AI tools may support this shift, especially in training resident doctors. The feasibility, accuracy, and acceptability of AI simplifying GP-directed letters into patient-directed versions is a promising research avenue.
