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作为健康数据的在线评论:以Yelp学术数据集为例研究医疗保健服务可及性与患者星级评分之间的关联

Online Reviews as Health Data: Examining the Association Between Availability of Health Care Services and Patient Star Ratings Exemplified by the Yelp Academic Dataset.

作者信息

Tran Nam N, Lee Joon

机构信息

Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.

出版信息

JMIR Public Health Surveill. 2017 Jul 12;3(3):e43. doi: 10.2196/publichealth.7001.

Abstract

BACKGROUND

There have been public health interventions that aim to reduce barriers to health care access by extending opening hours of health care facilities. However, the impact of opening hours from the patient's perspective is not well understood.

OBJECTIVE

This study aims to investigate the relationship between temporal accessibility of health care services and how patients rate the providers on Yelp, an online review website that is popular in the United States. Using crowdsourced open Internet data, such as Yelp, can help circumvent the traditional survey method.

METHODS

From Yelp's limited academic dataset, this study examined the pattern of visits to health care providers and performed a secondary analysis to examine the association between patient rating (measured by Yelp's rating) and temporal accessibility of health care services (measured by opening hours) using ordinal logistic regression models. Other covariates included were whether an appointment was required, the type of health care service, the region of the health care service provider, the number of reviews the health care service provider received in the past, the number of nearby competitors, the mean rating of competitors, and the standard deviation of competitors' ratings.

RESULTS

From the 2085 health care service providers identified, opening hours during certain periods, the type of health care service, and the variability of competitors' ratings showed an association with patient rating. Most of the visits to health care service providers took place between normal working hours (9 AM-5 PM) from Sunday to Thursday, and the least on Saturday. A model fitted to the entire sample showed that increasing hours during normal working hours on Monday (OR 0.926, 95% CI 0.880-0.973, P=0.03), Saturday (OR 0.897, 95% CI 0.860-0.935, P<0.001), Sunday (OR 0.904, 95% CI 0.841-0.970, P=0.005), and outside normal working hours on Friday (OR 0.872, 95% CI 0.760-0.998, P=0.048) was associated with receiving lower ratings. But increasing hours during outside normal working hours on Sunday was associated with receiving higher ratings (OR 1.400, 95% CI 1.036-1.924, P=0.03). There were also observed differences in patient ratings among the health care services types, but not geographically or by appointment requirement.

CONCLUSIONS

This study shows that public health interventions, especially those involving opening hours, could use crowdsourced open Internet data to enhance the evidence base for decision making and evaluation in the future. This study illustrates one example of how Yelp data could be used to understand patient experiences with health care services, making a case for future research for exploring online reviews as a health dataset.

摘要

背景

已有旨在通过延长医疗机构开放时间来减少医疗服务获取障碍的公共卫生干预措施。然而,从患者角度来看开放时间的影响尚未得到充分理解。

目的

本研究旨在调查医疗服务的时间可及性与患者在美国流行的在线评论网站Yelp上对医疗服务提供者的评分之间的关系。使用诸如Yelp之类的众包开放互联网数据有助于规避传统的调查方法。

方法

本研究从Yelp的有限学术数据集中,检查了对医疗服务提供者的就诊模式,并进行了二次分析,使用有序逻辑回归模型来检查患者评分(由Yelp评分衡量)与医疗服务的时间可及性(由开放时间衡量)之间的关联。纳入的其他协变量包括是否需要预约、医疗服务类型、医疗服务提供者所在地区、该医疗服务提供者过去收到的评论数量、附近竞争对手的数量、竞争对手的平均评分以及竞争对手评分的标准差。

结果

在识别出的2085家医疗服务提供者中,特定时间段的开放时间、医疗服务类型以及竞争对手评分的变异性与患者评分相关。大多数对医疗服务提供者的就诊发生在周日至周四的正常工作时间(上午9点至下午5点),周六就诊最少。拟合整个样本的模型显示,周一正常工作时间延长(比值比[OR]0.926,95%置信区间[CI]0.880 - 0.973,P = 0.03)、周六(OR 0.897,95% CI 0.860 - 0.935,P < 0.001)、周日(OR 0.904,95% CI 0.841 - 0.970,P = 0.005)以及周五正常工作时间之外延长开放时间(OR 0.872,95% CI 0.760 - 0.998,P = 0.048)与获得较低评分相关。但周日正常工作时间之外延长开放时间与获得较高评分相关(OR 1.400,9�% CI 1.036 - 1.924,P = 0.03)。在不同医疗服务类型之间也观察到了患者评分的差异,但在地理位置或是否需要预约方面没有差异。

结论

本研究表明,公共卫生干预措施,尤其是那些涉及开放时间的措施,未来可利用众包开放互联网数据来加强决策和评估的证据基础。本研究说明了如何利用Yelp数据来了解患者对医疗服务的体验,为未来将在线评论作为健康数据集进行探索的研究提供了依据。

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