ArticleBMJ quality & safety2016
Measuring patient-perceived quality of care in US hospitals using Twitter.
Article in BMJ quality & safety, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.BMJ health & care informatics · 2021Pooled it
- What Do Patients Complain About Online: A Systematic Review and Taxonomy Framework Based on Patient Centeredness.Journal of medical Internet research · 2019Pooled it
- Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health Apps.Yearbook of medical informatics · 2017Pooled it
- Does Patient Experience Correlate With Overall Hospital Quality Ratings?Journal of patient experience · 2026Article
- Mapping online patient reviews onto a data-informed service-experience framework: a content analysis of tertiary hospitals in Wuxi, China.Frontiers in public health · 2026Article
- The Use of Natural Language Processing to Interpret Unstructured Patient Feedback on Health Services: Scoping Review.Journal of medical Internet research · 2025Article
- Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation.JMIR medical informatics · 2025Article
- Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model-Assisted Content Analysis: Mixed Methods Study.JMIR formative research · 2025Article
- Systemic transphobia and ongoing barriers to healthcare for transgender and nonbinary people: A historical analysis of #TransHealthFail.PLOS digital health · 2025Article
- Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.JMIR medical informatics · 2024Article
- The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.Journal of nursing management · 2024Article
- Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation.JMIR formative research · 2023Article
- Understanding online ratings of joint arthroplasty surgeons: A national cross-sectional analysis.Journal of clinical orthopaedics and trauma · 2023Article
- Web-Based Public Ratings of General Practitioners in Norway: Validation Study.JMIR formative research · 2023Article
- "Is a game really a reason for people to die?" Sentiment and thematic analysis of Twitter-based discourse on Indonesia soccer stampede.AIMS public health · 2023Article
- An exploratory study to understand how people use Twitter to share experiences or information about spinal stenosis.Chiropractic & manual therapies · 2022Article
- The Experiences of Stakeholders Using Social Media as a Tool for Health Service Design and Quality Improvement: A Scoping Review.International journal of environmental research and public health · 2022Article
- Understanding the Patient Experience: Analysis of 2-Word Assessment and Its Relationship to Likelihood to Recommend in Outpatient Hand Surgery.Hand (New York, N.Y.) · 2022Article
- Wisdom of the Experts Versus Opinions of the Crowd in Hospital Quality Ratings: Analysis of Hospital Compare Star Ratings and Google Star Ratings.Journal of medical Internet research · 2022Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundPatients 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.
objectiveTo 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.
design404 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
resultsRoughly 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).
conclusionsTweets 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.