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利用推特衡量美国医院患者感知的医疗质量。

Measuring patient-perceived quality of care in US hospitals using Twitter.

作者信息

Hawkins Jared B, Brownstein John S, Tuli Gaurav, Runels Tessa, Broecker Katherine, Nsoesie Elaine O, McIver David J, Rozenblum Ronen, Wright Adam, Bourgeois Florence T, Greaves Felix

机构信息

Center for Biomedical Informatics, Harvard Medical School, Boston, USA Informatics Program, Boston Children's Hospital, Boston, USA.

Center for Biomedical Informatics, Harvard Medical School, Boston, USA Informatics Program, Boston Children's Hospital, Boston, USA Department of Pediatrics, Harvard Medical School, Boston, USA.

出版信息

BMJ Qual Saf. 2016 Jun;25(6):404-13. doi: 10.1136/bmjqs-2015-004309. Epub 2015 Oct 13.


DOI:10.1136/bmjqs-2015-004309
PMID:26464518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4878682/
Abstract

BACKGROUND: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. DESIGN: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. KEY RESULTS: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). CONCLUSIONS: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.

摘要

背景:患者经常使用推特来分享他们接受医疗服务的体验反馈。识别和分析发送给医院的推文内容可能会提供一种全新的实时质量衡量标准,作为对传统基于调查的方法的补充。 目的:评估将推特作为一种补充数据流来衡量美国医院患者感知的医疗质量,并将患者对医院的看法与既定的质量指标进行比较。 设计:在一年时间里,针对2349家美国医院的404065条推文,使用机器学习方法被归类为与患者体验有关。使用自然语言处理计算这些推文的情感倾向。11602条推文被手动分类到患者体验主题中。最后,对有≥50条患者体验推文的医院进行调查,以了解它们如何利用推特与患者互动。 主要结果:美国大约一半的医院在推特上有账号。在针对这些医院的推文中,34725条(9.4%)与患者体验相关,涵盖了各种主题。对有≥50条患者体验推文的医院的分析表明,它们在推特上更活跃,更有可能低于医疗保险患者的全国中位数(p<0.001)且高于护士/患者比例的全国中位数(p=0.006),并且更有可能是一家非营利性医院(p<0.001)。在对医院特征进行调整后,我们发现推特情感倾向与医疗服务提供者和系统的医院消费者评估(HCAHPS)评分无关(但拥有推特账号有关),尽管与30天医院再入院率有微弱关联(p=0.003)。 结论:描述医院患者体验的推文涵盖了广泛的患者护理方面,可以使用自动化方法识别。这些推文代表了一个潜在的未开发的质量指标,可能对患者、研究人员、政策制定者和医院管理人员有价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b94/4893126/e9c513d20b6a/bmjqs-2015-004309f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b94/4893126/d9ab1f5c6c58/bmjqs-2015-004309f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b94/4893126/e9c513d20b6a/bmjqs-2015-004309f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b94/4893126/d9ab1f5c6c58/bmjqs-2015-004309f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b94/4893126/e9c513d20b6a/bmjqs-2015-004309f02.jpg

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本文引用的文献

[1]
Trends in patient satisfaction in Dutch university medical centers: room for improvement for all.

BMC Health Serv Res. 2015-3-19

[2]
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J Med Internet Res. 2015-2-23

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Proc Natl Acad Sci U S A. 2015-2-24

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J Med Internet Res. 2015-1-15

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Tweets about hospital quality: a mixed methods study.

BMJ Qual Saf. 2014-10

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Detecting emotional contagion in massive social networks.

PLoS One. 2014-3-12

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