Evidence map›Paper›PMID 26464518›Full record

ArticleBMJ quality & safety2016

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

Jared B Hawkins, John S Brownstein, Gaurav Tuli, Tessa Runels, Katherine Broecker, Elaine O Nsoesie, David J McIver, Ronen Rozenblum, Adam Wright, Florence T Bourgeois and 1 more

Open access · hybridAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 4 pooled it
32.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 173 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Jared B HawkinsCenter for Biomedical Informatics, Harvard Medical School, Boston, USA Informatics Program, Boston Children's Hospital, Boston, USA.
John S BrownsteinCenter for Biomedical Informatics, Harvard Medical School, Boston, USA Informatics Program, Boston Children's Hospital, Boston, USA Department of Pediatrics, Harvard Medical School, Boston, USA.
Gaurav TuliVirginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
Tessa RunelsInformatics Program, Boston Children's Hospital, Boston, USA.ORCID 0000-0003-1942-966X
Katherine BroeckerInformatics Program, Boston Children's Hospital, Boston, USA.ORCID 0000-0001-9872-9833
Elaine O NsoesieInformatics Program, Boston Children's Hospital, Boston, USA Department of Pediatrics, Harvard Medical School, Boston, USA Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA.
David J McIverInformatics Program, Boston Children's Hospital, Boston, USA.
Ronen RozenblumDepartment of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Adam WrightDepartment of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Florence T BourgeoisInformatics Program, Boston Children's Hospital, Boston, USA Department of Pediatrics, Harvard Medical School, Boston, USA.
Felix GreavesDepartment of Primary Care and Public Health, Imperial College London, London, UK.
Boston University · USBrigham and Women's Hospital · USImperial College London · GBVirginia Tech · US

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
A Platform for Modeling the Global Impact of Climate Change on Infectious DiseaseR01LM010812 · NLM · BOSTON CHILDREN'S HOSPITAL · PI BROWNSTEIN, JOHN S. · 2010 to 2015
$2.5M
A Framework for Integrating Multiple Data Sources for Modeling and Forecasting of Infectious DiseasesK01ES025438 · NIEHS · UNIVERSITY OF WASHINGTON · PI NSOESIE, ELAINE O. · 2014 to 2016
$322k
NIEHS NIH HHS K01 ES025438NLM NIH HHS R01 LM010812NLM NIH HHS T15 LM007092
6 · The paper itself

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.

Indexed as

Patient SatisfactionSocial MediaAdultHospitalsHumansQuality Assurance, Health CareQuality of Health CareUnited StatesHealthcare quality improvementPatient satisfactionPerformance measuresQuality improvement methodologiesQuality measurement

Identifiers

PMID26464518
PMCPMC4878682
OpenAlexW1955303603

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.