Introduction

An increasing number of patients are voicing their opinions and expectations about the quality of care in online forums and on physician rating websites (PRWs). This paper analyzes patient online reviews (PORs) to identify emerging and fading topics and sentiment trends in PRWs during the early stage of the COVID-19 outbreak.

Methods

Text data were collected, including 55,612 PORs of 3430 doctors from three popular PRWs in the United States (RateMDs, HealthGrades, and Vitals) from March 01 to June 27, 2020. An improved latent Dirichlet allocation (LDA)-based topic modeling (topic coherence-based LDA [TCLDA]), manual annotation, and sentiment analysis tool were applied to extract a suitable number of topics, generate corresponding keywords, assign topic names, and determine trends in the extracted topics and specific emotions.

Results

According to the coherence value and manual annotation, the identified taxonomy includes 30 topics across high-rank and low-rank disease categories. The emerging topics in PRWs focus mainly on themes such as treatment experience, policy implementation regarding epidemic control measures, individuals’ attitudes toward the pandemic, and mental health across high-rank diseases. In contrast, the treatment process and experience during COVID-19, awareness and COVID-19 control measures, and COVID-19 deaths, fear, and stress were the most popular themes for low-rank diseases. Panic buying and daily life impact, treatment processes, and bedside manner were the fading themes across high-rank diseases. In contrast, provider attitude toward patients during the pandemic, detection at public transportation, passenger, travel bans and warnings, and materials supplies and society support during COVID-19 were the most fading themes across low-rank diseases. Regarding sentiment analysis, negative emotions (fear, anger, and sadness) prevail during the early wave of the COVID-19.

Conclusion

Mining topic dynamics and sentiment trends in PRWs may provide valuable knowledge of patients’ opinions during the COVID-19 crisis. Policymakers should consider these PORs and develop global healthcare policies and surveillance systems through monitoring PRWs. The findings of this study identify research gaps in the areas of e-health and text mining and offer future research directions.

中文翻译：

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在 COVID-19 大流行的早期浪潮中挖掘医生评级网站中的主题和情绪动态：机器学习方法

介绍

越来越多的患者在在线论坛和医生评级网站 (PRW) 上表达他们对护理质量的意见和期望。本文分析了患者在线评论 (POR)，以确定在 COVID-19 爆发早期阶段 PRW 中出现和消失的主题和情绪趋势。

方法

收集了文本数据，包括 2020 年 3 月 1 日至 6 月 27 日期间来自美国三个流行的 PRW（RateMDs、HealthGrades 和 Vitals）的 3430 名医生的 55,612 个 POR。改进的基于潜在狄利克雷分配 (LDA) 的主题建模（topic基于一致性的LDA [TCLDA]），手动标注和情感分析工具被应用于提取合适数量的主题，生成相应的关键词，分配主题名称，并确定提取的主题和特定情感的趋势。

结果

根据一致性值和人工注释，识别的分类包括 30 个主题，跨越高等级和低等级疾病类别。PRW中的新兴话题主要集中在治疗经验、疫情控制措施的政策执行、个人对大流行的态度以及跨高阶疾病的心理健康等主题。相比之下，COVID-19 期间的治疗过程和经验、认识和 COVID-19 控制措施以及 COVID-19 死亡、恐惧和压力是低等级疾病最受欢迎的主题。恐慌性购买和日常生活影响、治疗过程和床边态度是高级疾病中逐渐消失的主题。相比之下，提供者在大流行期间对患者的态度、在公共交通中的检测、乘客、旅行禁令和警告，在 COVID-19 期间，材料供应和社会支持是低级别疾病中最褪色的主题。关于情绪分析，负面情绪（恐惧、愤怒和悲伤）在 COVID-19 的早期浪潮中盛行。

结论

挖掘 PRW 中的主题动态和情绪趋势可能会在 COVID-19 危机期间提供有关患者意见的宝贵知识。政策制定者应考虑这些 POR，并通过监测 PRW 制定全球医疗保健政策和监测系统。本研究的结果确定了电子健康和文本挖掘领域的研究差距，并提供了未来的研究方向。