Evidence map›Paper›PMID 42476594›Full record

ArticleBMJ open quality2026

Understanding online hospital patient reviews: a nationwide cross-sectional analysis of polarity, actionability and themes.

Siu Yu Zoe Lau, Matthew P Tse, Irfan Dhalla, Dhruv Nayyar

Abstract read
In one paragraph

Article in BMJ open quality, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Siu Yu Zoe LauGeneral Internal Medicine, University Health Network, Toronto, Ontario, Canada siuyu.lau@uhn.ca.ORCID http://orcid.org/0009-0005-1167-5892
Matthew P TseUnity Health Toronto, Toronto, Ontario, Canada.ORCID http://orcid.org/0009-0004-1544-3784
Irfan DhallaDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Dhruv NayyarDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient experience data can support hospital quality improvement initiatives. Unsolicited online hospital reviews have emerged as an alternative data source in addition to traditional survey methods. However, the content and actionability of qualitative online reviews for Canadian hospitals are unknown.

methodsWe conducted a nationwide cross-sectional study analysing Google Review text comments of Canadian acute care hospitals from 2017 to 2022. We analysed a random sample of 1000 comments from June 2017 to June 2022. We characterised the polarity, actionability and thematic content of these comments.

resultsPatients were more likely to leave negative reviews (47.9%) than mixed (19.8%) or positive (32.3%) reviews. Most reviews (74.0%) were semiactionable or very actionable. Negative or mixed reviews were generally more actionable than positive reviews (p<0.001). Lastly, patients were most likely to comment on interpersonal interactions with staff and wait times for care.

conclusionsQualitative online patient reviews contain actionable feedback. This can provide a source of real-time data to monitor and improve on patient experience.

Indexed as

HospitalsPatient SatisfactionCanadaCross-Sectional StudiesHumansInternetQualitative ResearchQuality ImprovementSurveys and QuestionnairesHealthcare quality improvementPatient satisfactionQuality improvement

Identifiers

PMID42476594
PMCPMC13386053

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.