Evidence map›Paper›PMID 37292342›Full record

ArticleHeliyon2023

Face time with physicians: How do patients assess providers in video-visits?

Ranganathan Chandrasekaran, Prathamesh Bapat, Pruthivinath Jeripity Venkata, Evangelos Moustakas

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

4 authors.

Ranganathan ChandrasekaranDepartment of Information & Decision Sciences, University of Illinois at Chicago, USA.
Prathamesh BapatDepartment of Information & Decision Sciences, University of Illinois at Chicago, USA.
Pruthivinath Jeripity VenkataDepartment of Information & Decision Sciences, University of Illinois at Chicago, USA.
Evangelos MoustakasHeriot-Watt University, Dubai.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The COVID-19 pandemic has triggered a massive acceleration in the use of virtual and video-visits. As more patients and providers engage in video-visits over varied digital platforms, it is important to understand how patients assess their providers and the video-visit experiences. We also need to examine the relative importance of the factors that patients use in their assessment of video-visits in order to improve the overall healthcare experience and delivery. Methods: A data set of 5149 reviews of patients completing a video-visit was assembled through web scraping. Sentiment analysis was performed on the reviews and topic modeling was used to extract latent topics embedded in the reviews and their relative importance. Results: Most patient reviews (89.53%) reported a positive sentiment towards their providers in video-visits. Seven distinct topics underlying the reviews were identified: bedside manners, professional expertise, virtual experience, appointment scheduling and follow-up process, wait times, costs, and communication. Communication, bedside manners and professional expertise were the top factors patients alluded to in the positive reviews. Appointment-scheduling and follow-ups, wait-times, costs, virtual experience and professional expertise were important factors in the negative reviews. Discussion: To improve the overall experience of patients in video-visits, providers need to engage in clear communication, grow excellent bedside and

Indexed as

Physician reviewsTelehealthTelemedicineText miningTopic modellingVideo visits

Identifiers

PMID37292342
PMCPMC10238118

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.