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    Title: 基於患者滿意度之線上醫療諮詢醫生回應後設語篇分析與提示工程應用
    Metadiscourse Analysis of Doctor Responses Based on Patient Satisfaction in Online Medical Consultations and Its Applications in Prompt Engineering
    Authors: 黃靖涵
    Huang, Ching-Han
    Contributors: 張瑜芸
    許展嘉

    Chang, Yu-Yun
    Hsu, Chan-Chia

    黃靖涵
    Huang, Ching-Han
    Keywords: 線上醫療諮詢
    後設語篇
    同理心
    滿意度
    提示工程
    Online medical consultation
    Metadiscourse
    Empathy
    Satisfaction
    Prompt engineering
    Date: 2025
    Issue Date: 2025-08-04 15:05:34 (UTC+8)
    Abstract: 本研究旨在探討台灣華語線上醫療諮詢裡醫師回應的後設語篇策略如何影響病患滿意度,並嘗試將研究發現應用於病患滿意度評估的提示工程。本研究採用 Hyland (2005) 的後設語篇理論框架,觀察交互式標記與互動式標記的使用,探究醫師回應中的組織結構與互動模式。本研究語料來源為台灣e院線上醫療諮詢平台,收集具有病患滿意度評分的醫師回應,建為語料庫,進行量化與質化分析。量化分析結果顯示,不同滿意度回應間在交互式與互動式後設語篇標記的使用上均呈現顯著差異。質化分析發現,令人滿意的回應更頻繁地使用明確的因果標記、個人化指涉與同理心表達,而不滿意的回應則傾向模糊的表達方式,且缺乏互動性。將這些研究發現應用於 GPT-4o 的病患滿意度分類提示設計,結果顯示思維鏈提示在此任務中表現最佳。本研究透過探討醫師回應中的語言策略與病患滿意度之間的關聯性,為台灣華語線上醫療諮詢溝通研究提供貢獻,並展示如何將這些分析結果應用於自動化評估患者滿意度的提示工程。
    This study investigates how metadiscourse strategies in doctors’ responses influence patient satisfaction in Taiwan Mandarin Online Medical Consultations (OMC), and explores the application of these insights to prompt engineering for automated patient satisfaction assessment. This study relies on Hyland’s (2005) framework to analyze the organization and interaction of doctors’ responses, examining the use of interactive and interactional devices. Based on a self-compiled corpus of doctor responses with patient satisfaction ratings from Taiwan e-Hospital, this study conducts both quantitative and qualitative analyses. The quantitative analysis reveals significant linguistic differences between the satisfactory and unsatisfactory groups in their use of both interactive and interactional metadiscourse devices. The qualitative analysis shows that satisfactory responses more frequently employ explicit causal transitions and personalized references with empathetic expressions, while unsatisfactory responses tend to be vague and less interactive. Incorporating these insights into GPT-4o prompt design for patient satisfaction classification, this study shows that Chain-of-Thought prompting yields the best performance. The study contributes to OMC communication research by detailing how doctors’ linguistic strategies correlate with patients’ satisfaction, and demonstrates how such insights can optimize prompt engineering for automated evaluation.
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    國立政治大學
    語言學研究所
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