Patient Experience and Satisfaction in Online Reviews of Obstetric Care: Observational Study.

ML Yelp delivery labor labor and delivery machine learning maternal health natural language processing ob-gyn obstetrics online reviews patient satisfaction patients quality improvement

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
31 Mar 2022
Historique:
received: 03 03 2021
accepted: 13 12 2021
revised: 29 06 2021
entrez: 31 3 2022
pubmed: 1 4 2022
medline: 1 4 2022
Statut: epublish

Résumé

The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P<.001), describing staff as comforting (r=0.52, P<.001), the delivery experience (r=0.46, P<.001), modern and clean facilities (r=0.44, P<.001), and hospital food (r=0.38, P<.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (r=0.30, P<.001), a lack of agency among patients (r=0.47, P<.001), and issues with discharging from the hospital (r=0.32, P<.001). Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.

Sections du résumé

BACKGROUND BACKGROUND
The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public.
OBJECTIVE OBJECTIVE
The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp.
METHODS METHODS
We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings.
RESULTS RESULTS
A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P<.001), describing staff as comforting (r=0.52, P<.001), the delivery experience (r=0.46, P<.001), modern and clean facilities (r=0.44, P<.001), and hospital food (r=0.38, P<.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (r=0.30, P<.001), a lack of agency among patients (r=0.47, P<.001), and issues with discharging from the hospital (r=0.32, P<.001).
CONCLUSIONS CONCLUSIONS
Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.

Identifiants

pubmed: 35357310
pii: v6i3e28379
doi: 10.2196/28379
pmc: PMC9015735
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e28379

Subventions

Organisme : NIDA NIH HHS
ID : R21 DA050761
Pays : United States

Informations de copyright

©Emily K Seltzer, Sharath Chandra Guntuku, Amy L Lanza, Christopher Tufts, Sindhu K Srinivas, Elissa V Klinger, David A Asch, Nick Fausti, Lyle H Ungar, Raina M Merchant. Originally published in JMIR Formative Research (https://formative.jmir.org), 31.03.2022.

Références

J Patient Exp. 2014 May;1(1):20-22
pubmed: 28725797
N Engl J Med. 2017 Jan 19;376(3):197-199
pubmed: 28099823
BMJ Qual Saf. 2016 Jan;25(1):14-24
pubmed: 26208538
Health Aff (Millwood). 2016 Apr;35(4):697-705
pubmed: 27044971
J Gen Intern Med. 2019 Jul;34(7):1079-1080
pubmed: 30767117
Health Serv Res. 2005 Dec;40(6 Pt 2):2078-95
pubmed: 16316439
JAMA. 2016 Dec 20;316(23):2483-2484
pubmed: 27997663
Am J Obstet Gynecol. 2004 Jan;190(1):175-82
pubmed: 14749656
Ann Emerg Med. 2019 Jun;73(6):631-638
pubmed: 30392737
Obstet Gynecol. 2015 Aug;126(2):255-257
pubmed: 26241412
BMJ Qual Saf. 2016 Nov;25(11):889-897
pubmed: 26677215
Neurosurgery. 2012 Aug;71(2):N21-4
pubmed: 22811203
JAMA. 2017 Feb 21;317(7):766-768
pubmed: 28241346
Lancet. 2001 Mar 10;357(9258):757-62
pubmed: 11253970
BMJ Qual Saf. 2013 Mar;22(3):194-202
pubmed: 23178860
J Med Internet Res. 2016 Sep 19;18(9):e254
pubmed: 27644135
N Engl J Med. 2015 Aug 13;373(7):675-9
pubmed: 26267629

Auteurs

Emily K Seltzer (EK)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.

Sharath Chandra Guntuku (SC)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.

Amy L Lanza (AL)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.

Christopher Tufts (C)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.

Sindhu K Srinivas (SK)

Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, United States.

Elissa V Klinger (EV)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.

David A Asch (DA)

Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.
Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States.

Nick Fausti (N)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.

Lyle H Ungar (LH)

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.

Raina M Merchant (RM)

Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.

Classifications MeSH