Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study - Takeaways - MDSpire

Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study

  • By

  • Zhe He

  • Balu Bhasuran

  • Mia Liza A Lustria

  • Karim Hanna

  • Michael Killian

  • Cindy Shavor

  • Mandy Dailey

  • Sai Sidharth Manikandan

  • Xiao Luo

  • July 9, 2026

  • 0 min

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  • 1

    The 21st Century Cures Act mandates near real-time access to EHR data for patients, enhancing transparency in healthcare.

  • 2

    Patient portals facilitate access to laboratory results, but often present data in a clinician-oriented format that may confuse patients.

  • 3

    Question prompt lists (QPLs) improve patient engagement but are typically static and not tailored to individual clinical contexts.

  • 4

    Large language models (LLMs) have shown potential in generating patient-friendly questions based on EHR data, enhancing communication.

  • 5

    Studies indicate that LLM-generated responses to patient messages can be perceived as clearer and more compassionate than physician-authored replies.

Original Source(s)

Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study

  • by Zhe He, Balu Bhasuran, Mia Liza A Lustria, Karim Hanna, Michael Killian, Cindy Shavor, Mandy Dailey, Sai Sidharth Manikandan, Xiao Luo

  • July 9, 2026

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