在马来西亚塞兰戈尔的私立医院的基于网络的评论中分析患者投诉,使用大语言模型辅助内容分析:混合方法研究
Muhammad Hafiz Sulaiman1,2, Nora Muda1, Fatimah Abdul Razak1
1Department of Mathematical Sciences, Faculty of Science and Technology, National University of Malaysia, Bangi, Malaysia.
JMIR formative research
|June 27, 2025
概括
大型语言模型 (LLM) 辅助的内容分析 (LACA) 为分析医院评论提供了一种经济有效和高效的方法. 这种方法可靠地识别了患者反中的关键主题,帮助医院管理人员迅速生成假设.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 传统的内容分析是耗时和资源密集的.
- 大型语言模型 (LLM) 提供了自动化和增强内容分析的潜力.
- LLM辅助内容分析 (LACA) 是一种新的方法,利用LLM进行主题编码.
研究的目的:
- 开发和验证LACA用于分析医院基于Web的评论.
- 通过LACA使用医院基于网络的审查来确定问题的关键主题.
- 与传统方法相比,评估LACA的可靠性和效率.
主要方法:
- 获取了马来西亚53家私立医院的14938条基于网络的评论.
- 使用NLP和机器学习过虚假评论;使用GPT-4o mini API删除低质量的评论.
- 通过并行人类-LLM编码 (κ=0.81) 开发了一个经过验证的代码书,并对主题应用了因子分析.
主要成果:
- 确定了6个关键主题:服务/沟通,临床护理,设施,预约,财务和患者权利.
- 在人类编码者和LLM之间实现了高的interrater可靠性 (κ=0.81).
- 因子分析揭示了可解释的潜伏因子,具有很高的可靠性 (克朗巴赫的α 0.61-0.97).
结论:
- 使用Python Selenium,GPT-4o mini API和因子分析的管道使有效和可靠的专题分析成为可能.
- 基于网络的LACA审查是成本高效的,时间高效的,并支持实时的假设生成医院管理人员.
- 尽管存在潜在的偏见,但LACA为了解患者体验和改善医院服务提供了宝贵的工具.
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