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Harnessing the cloud of patient experience: using social media to detect poor quality healthcare
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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Metadata
- Authors
- Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Greaves, Felix
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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