Study Design: 

A quantitative analysis of written, online reviews of Cervical Spine Research Society (CSRS) surgeons.

Objective: 

This study quantitatively analyzes the written reviews of members of the CSRS to report biases associated with demographic factors and frequently used words in reviews to help aid physician practices.

Summary of Background Data: 

Physician review websites have influence on a patient’s selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback.

Methods: 

Online written and star-rating reviews of CSRS surgeons were obtained from healthgrades.com. A sentiment analysis package was used to obtain compound scores of each physician’s reviews. The relationship between demographic variables and average sentiment score of written reviews were evaluated through t-tests. Positive and negative word and bigram frequency analysis was performed to indicate trends in the reviews’ language.

Results: 

In all, 2239 CSRS surgeon’s reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (r²=0.60, P<0.01). There was no difference in review sentiment by provider sex. However, the age of surgeons showed a significant difference as those <55 had more positive reviews (mean=+0.50) than surgeons >=55 (mean=+0.37) (P<0.01). The most positive reviews focused both on pain and behavioral factors, whereas the most negative focused mainly on pain. Behavioral attributes increased the odds of receiving positive reviews while pain decreased them.

Conclusion: 

The top-rated surgeons were described as considerate providers and effective at managing pain in their most frequently used words and bigrams. However, the worst-rated ones were mainly described as unable to relieve pain. Through quantitative analysis of physician reviews, pain is a clear factor contributing to both positive and negative reviews of surgeons, reinforcing the need for proper pain expectation management.

Level of Evidence: 

Level 4—retrospective case-control study.

中文翻译：

---

患者如何在线描述您？CSRS 外科医生在线评论的自然语言处理驱动情感分析

学习规划： 

对颈椎研究协会 (CSRS) 外科医生的书面在线评论进行定量分析。

客观的： 

本研究定量分析了 CSRS 成员的书面评论，以报告与人口统计因素和评论中常用词相关的偏见，以帮助医生执业。

背景数据摘要： 

医师评论网站会影响患者对提供者的选择，但书面评论是主观的。通过人工智能对写作进行情感分析可以量化外科医生的评论，以提供可操作的反馈。

方法： 

CSRS 外科医生的在线书面和星级评价来自 healthgrades.com。情绪分析包被用来获得每个医生评论的复合分数。通过t检验评估人口统计变量与书面评论的平均情绪得分之间的关​​系。执行正面和负面单词和二元组频率分析以指示评论语言的趋势。

结果： 

总共分析了 2239 名 CSRS 外科医生的评论。分析表明，情感得分与总体平均星级评论之间存在正相关关系（r ² =0.60，P <0.01）。提供者性别的评论情绪没有差异。然而，外科医生的年龄显示出显着差异，<55 岁的外科医生比>=55 岁的外科医生（平均=+0.37）有更多的正面评价（平均值=+0.50）（P <0.01 ）。最正面的评论集中在疼痛和行为因素上，而最负面的评论主要集中在疼痛上。行为属性增加了获得正面评价的几率，而痛苦则降低了它们。

结论： 

评价最高的外科医生被描述为体贴的提供者，并且在他们最常用的单词和二元组中有效地控制疼痛。然而，评分最差的主要被描述为无法缓解疼痛。通过对医生评论的定量分析，疼痛是影响外科医生正面和负面评论的一个明显因素，加强了对适当疼痛预期管理的需求。

证据等级： 

4 级——回顾性病例对照研究。