Evidence map›Paper›PMID 35357310›Full record

ArticleJMIR formative research2022

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

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

Abstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Emily K SeltzerPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-9879-4739
Sharath Chandra GuntukuPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-2929-0035
Amy L LanzaPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-3647-4390
Christopher TuftsPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-7332-9358
Sindhu K SrinivasDepartment of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-7655-3013
Elissa V KlingerPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-1311-0826
David A AschPenn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-7970-286X
Nick FaustiPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-6579-5209
Lyle H UngarDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-2047-1443
Raina M MerchantPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0002-9801-6881

Funding

Analyzing Online Reviews to Evaluate Quality of Care at Substance Use Disorder Treatment FacilitiesR21DA050761 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI MERCHANT, RAINA · 2020 to 2021
$446k
NIDA NIH HHS R21 DA050761
6 · The paper itself

Abstract

backgroundThe 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.

objectiveThe aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp.

methodsWe 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.

resultsA 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).

conclusionsOnline 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.

Indexed as

deliverylaborlabor and deliverymachine learningmaternal healthMLnatural language processingob-gynobstetricsonline reviewspatientspatient satisfactionquality improvementYelp

Identifiers

PMID35357310
PMCPMC9015735

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.