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

被引:101
作者
Hawkins, Jared B. [1 ,2 ]
Brownstein, John S. [1 ,2 ,3 ]
Tuli, Gaurav [4 ,5 ]
Runels, Tessa [2 ]
Broecker, Katherine [2 ]
Nsoesie, Elaine O. [2 ,3 ,6 ]
McIver, David J. [2 ]
Rozenblum, Ronen [7 ,8 ]
Wright, Adam [7 ,8 ]
Bourgeois, Florence T. [2 ,3 ]
Greaves, Felix [9 ]
机构
[1] Harvard Univ, Sch Med, Ctr Biomed Informat, Boston, MA USA
[2] Boston Childrens Hosp, Informat Program, Boston, MA USA
[3] Harvard Univ, Sch Med, Dept Pediat, Boston, MA 02115 USA
[4] Virginia Tech, Virginia Bioinformat Inst, Blacksburg, VA USA
[5] Virginia Tech, Comp Sci, Blacksburg, VA USA
[6] Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA
[7] Brigham & Womens Hosp, Dept Med, 75 Francis St, Boston, MA 02115 USA
[8] Harvard Univ, Sch Med, Dept Med, Boston, MA USA
[9] Univ London Imperial Coll Sci Technol & Med, Dept Publ Hlth & Primary Care, London, England
关键词
SOCIAL MEDIA; HEALTH-CARE; SENTIMENT ANALYSIS; READMISSION; AGREEMENT; FACEBOOK;
D O I
10.1136/bmjqs-2015-004309
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:404 / 413
页数:10
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