Cited by 273Patient Satisfaction in HealthcareSentiment Analysis and Opinion MiningSocial Media in Health Education

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Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

Greaves, Felix, Ramirez-Cano, Daniel, Millett, Christopher, Darzi, Ara, Donaldson, Liam·2013·BMJ Quality & Safety

DOI 10.1136/bmjqs-2012-001527

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Abstract

Recent years have seen increasing interest in patient-centred care and calls to focus on improving the patient experience. At the same time, a growing number of patients are using the internet to describe their experiences of healthcare. We believe the increasing availability of patients’ accounts of their care on blogs, social networks, Twitter and hospital review sites presents an intriguing opportunity to advance the patient-centred care agenda and provide novel quality of care data. We describe this concept as a ‘cloud of patient experience’. In this commentary, we outline the ways in which the collection and aggregation of patients’ descriptions of their experiences on the internet could be used to detect poor clinical care. Over time, such an approach could also identify excellence and allow it to be built on. We suggest using the techniques of natural language processing and sentiment analysis to transform unstructured descriptions of patient experience on the internet into usable measures of healthcare performance. We consider the various sources of information that could be used, the limitations of the approach and discuss whether these new techniques could detect poor performance before conventional measures of healthcare quality.

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Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Big Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647
Untitled work
(2012) · PubMed · Cited by 2,326
Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet
Eysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351
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Mislove, A. E., Lehmann, S., Ahn, Y., Onnela, J.-P. “., & Rosenquist, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14168 · Cited by 761
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Murff, H. J., FitzHenry, F., Matheny, M. E., Gentry, N., Kotter, K., Crimin, K. S., Dittus, R. S., Rosen, A. K., Elkin, P. L., Brown, S. H., et al. (2011) · JAMA · doi:10.1001/jama.2011.1204 · Cited by 475
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Show all 21 references

Research works printed but not linked: 1

Cambria E Hussain A Havasi C . Towards the crowd validation of the British National Health Service. Proceedings of WebSci10: Extending the Frontiers of Society On-Line; 26–27 April 2010, Raleigh, NC, USA, 2010.

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Authors: Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L.
Affiliations: Imperial College London (GB)
Corresponding author: Greaves, Felix
ORCID: Greaves, Felix
Millett, Christopher
Darzi, Ara
Donaldson, Liam
Venue: BMJ Quality & Safety
Publisher: BMJ
Published: 2013-01-24
Volume: 22
Issue: 3
Pages: 251-255
ISSN: 1475-3898 (ISSN-L)
2044-5415
2044-5423
DOI: 10.1136/bmjqs-2012-001527
OpenAlex: W2144127426
Cited by: 272
References: 21
Citations by year: 2026: 6 · 2025: 10 · 2024: 16 · 2023: 13 · 2022: 9 · 2021: 25 · 2020: 22 · 2019: 24 · 2018: 38 · 2017: 27 · 2016: 18 · 2015: 27 · 2014: 23 · 2013: 14
FWCI: 49.31
Type: journal article
Language: en
Open access: closed
Locations: BMJ Quality & Safety (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute for Health and Care Research (NF-SI-0510-10186, ACF-2009-21-030)
Topics: Health Sciences › Health Professions › General Health Professions › Patient Satisfaction in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Social Sciences › Health › Social Media in Health Education
Keywords: patient experience, social media, quality of care, patient-centered care, online patient reviews, blogs, Twitter, natural language processing, sentiment analysis, healthcare performance measurement
MeSH terms: Humans, Inpatients, National Health Programs, Quality of Health Care, Patient Satisfaction, Patient-Centered Care, Social Media

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Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

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