用AI分析在线评价，发现医护态度和效率是急症就医满意度关键
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction
- 用GPT模型分析地图评论，按人际、效率、技术等维度做情感剖析
- 调整多变量后发现，只有人际互动和运营效率显著影响满意度
- 适合医疗管理者、政策制定者参考，了解民众真实关切点
研究急症护理设施的公众体验对推动社区医疗发展至关重要。传统调查方法因范围、时间和空间覆盖有限而效果不佳。通过在线评论或社交媒体进行众包，可有效获取此类洞察。随着大语言模型（LLMs）的发展，从评论中提取细微感知成为可能。本研究收集了美国DMV和佛罗里达地区谷歌地图上的评论，采用GPT模型进行提示工程，分析急症护理的方面级情感。首先，分析了人际因素、运营效率、技术质量、财务状况和设施条件等各方面的地理空间分布模式；其次，探讨了普查街区组（CBG）层面特征对公众感知差异的影响，包括人口密度、中位收入、基尼系数、租金收入比、贫困家庭比例、无保险率及失业率。结果表明，在多变量模型中，人际因素和运营效率是患者满意度最强的决定因素，而技术质量、财务和设施条件则无显著独立影响。在社会经济与人口统计因素中，仅人口密度与患者评分存在显著但微弱的相关性，其余因素均无显著关联。总体而言，该研究展示了众包数据在揭示居民关注重点方面的潜力，并为相关利益方提升急症护理满意度提供了重要参考。
原文摘要 · Abstract (English)
Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limited scope, time, and spatial coverage. Crowdsourcing through online reviews or social media offers a valuable approach to gaining such insights. With recent advancements in large language models (LLMs), extracting nuanced perceptions from reviews has become feasible. This study collects Google Maps reviews across the DMV and Florida areas and conducts prompt engineering with the GPT model to analyze the aspect-based sentiment of urgent care. We first analyze the geospatial patterns of various aspects, including interpersonal factors, operational efficiency, technical quality, finances, and facilities. Next, we determine Census Block Group (CBG)-level characteristics underpinning differences in public perception, including population density, median income, GINI Index, rent-to-income ratio, household below poverty rate, no insurance rate, and unemployment rate. Our results show that interpersonal factors and operational efficiency emerge as the strongest determinants of patient satisfaction in urgent care, while technical quality, finances, and facilities show no significant independent effects when adjusted for in multivariate models. Among socioeconomic and demographic factors, only population density demonstrates a significant but modest association with patient ratings, while the remaining factors exhibit no significant correlations. Overall, this study highlights the potential of crowdsourcing to uncover the key factors that matter to residents and provide valuable insights for stakeholders to improve public satisfaction with urgent care.
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