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PatientEase-Domain-Aware RAG for Rehabilitation Instruction Simplification.

Rashid Nasimov1, Akmalbek Abdusalomov2,3,4,5, Charos Khidirova2

  • 1Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.

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PubMed
Summary
This summary is machine-generated.

PatientEase simplifies rehabilitation instructions for better patient understanding and adherence. This AI framework enhances communication, improving safety and care in clinical settings.

Keywords:
clinical natural language processing (NLP)domain adaptationmedical text simplificationmulti-agent coordinationpatient-centered healthcare communicationrehabilitation informaticsreinforcement learning from human feedback (RLHF)retrieval-augmented generation (RAG)

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Patient Education

Background:

  • Rehabilitation relies on instructional materials often difficult for patients to comprehend, impacting adherence to care.
  • Existing text simplification systems lack focus on procedure-oriented guidance and personalization crucial for rehabilitation.

Purpose of the Study:

  • To develop PatientEase, a novel framework for simplifying rehabilitation instructions while preserving clinical meaning.
  • To enhance patient comprehension and adherence through personalized, easy-to-understand guidance.

Main Methods:

  • PatientEase utilizes a domain-aware retrieval-augmented generation framework with specialized corpus and user-aligned retrievers.
  • A role-structured, multi-agent rewriting pipeline refines instructions, with optional reinforcement learning from human feedback for improved quality.

Main Results:

  • PatientEase demonstrated high performance in benchmark tests, achieving 52.7 SARI and 92.1% factual entailment.
  • The system received top ratings for fluency and simplicity, with ablations confirming the importance of each module.

Conclusions:

  • PatientEase promotes safer, patient-centered communication in rehabilitation settings.
  • The framework establishes a foundation for developing trustworthy clinical dialogue systems.