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

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 us...

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Autores principales: Hawkins, Jared B, Brownstein, John S, Tuli, Gaurav, Runels, Tessa, Broecker, Katherine, Nsoesie, Elaine O, McIver, David J, Rozenblum, Ronen, Wright, Adam, Bourgeois, Florence T, Greaves, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878682/
https://www.ncbi.nlm.nih.gov/pubmed/26464518
http://dx.doi.org/10.1136/bmjqs-2015-004309
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author Hawkins, Jared B
Brownstein, John S
Tuli, Gaurav
Runels, Tessa
Broecker, Katherine
Nsoesie, Elaine O
McIver, David J
Rozenblum, Ronen
Wright, Adam
Bourgeois, Florence T
Greaves, Felix
author_facet Hawkins, Jared B
Brownstein, John S
Tuli, Gaurav
Runels, Tessa
Broecker, Katherine
Nsoesie, Elaine O
McIver, David J
Rozenblum, Ronen
Wright, Adam
Bourgeois, Florence T
Greaves, Felix
author_sort Hawkins, Jared B
collection PubMed
description 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.
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spelling pubmed-48786822016-06-01 Measuring patient-perceived quality of care in US hospitals using Twitter Hawkins, Jared B Brownstein, John S Tuli, Gaurav Runels, Tessa Broecker, Katherine Nsoesie, Elaine O McIver, David J Rozenblum, Ronen Wright, Adam Bourgeois, Florence T Greaves, Felix BMJ Qual Saf Original Research 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. BMJ Publishing Group 2016-06 2015-10-13 /pmc/articles/PMC4878682/ /pubmed/26464518 http://dx.doi.org/10.1136/bmjqs-2015-004309 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Original Research
Hawkins, Jared B
Brownstein, John S
Tuli, Gaurav
Runels, Tessa
Broecker, Katherine
Nsoesie, Elaine O
McIver, David J
Rozenblum, Ronen
Wright, Adam
Bourgeois, Florence T
Greaves, Felix
Measuring patient-perceived quality of care in US hospitals using Twitter
title Measuring patient-perceived quality of care in US hospitals using Twitter
title_full Measuring patient-perceived quality of care in US hospitals using Twitter
title_fullStr Measuring patient-perceived quality of care in US hospitals using Twitter
title_full_unstemmed Measuring patient-perceived quality of care in US hospitals using Twitter
title_short Measuring patient-perceived quality of care in US hospitals using Twitter
title_sort measuring patient-perceived quality of care in us hospitals using twitter
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878682/
https://www.ncbi.nlm.nih.gov/pubmed/26464518
http://dx.doi.org/10.1136/bmjqs-2015-004309
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