ArticleJournal of general internal medicine2019
A Comparison of Online Physician Ratings and Internal Patient-Submitted Ratings from a Large Healthcare System.
Article in Journal of general internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- Coverage, Traits, and Geographic Distribution of Online Surgeon Reviews: Large-Scale Cross-Sectional Analysis.JMIR formative research · 2026Article
- Article
- The Use of Web-Based Patient Reviews to Assess Medical Oncologists' Competency: Mixed Methods Sequential Explanatory Study.JMIR formative research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhysician online ratings are ubiquitous and influential, but they also have their detractors. Given the lack of scientific survey methodology used in online ratings, some health systems have begun to publish their own internal patient-submitted ratings of physicians.
objectiveThe purpose of this study was to compare online physician ratings with internal ratings from a large healthcare system.
designRetrospective cohort study comparing online ratings with internal ratings from a large healthcare system.
settingKaiser Permanente, a large integrated healthcare delivery system.
participantsPhysicians in the Southern California region of Kaiser Permanente, including all specialties with ambulatory clinic visits. MAIN MEASURES: The primary outcome measure was correlation between online physician ratings and internal ratings from the integrated healthcare delivery system.
resultsOf 5438 physicians who met inclusion and exclusion criteria, 4191 (77.1%) were rated both online and internally. The online ratings were based on a mean of 3.5 patient reviews, while the internal ratings were based on a mean of 119 survey returns. The overall correlation between the online and internal ratings was weak (Spearman's rho .23), but increased with the number of reviews used to formulate each online rating.
conclusionsPhysician online ratings did not correlate well with internal ratings from a large integrated healthcare delivery system, although the correlation increased with the number of reviews used to formulate each online rating. Given that many consumers are not aware of the statistical issues associated with small sample sizes, we would recommend that online rating websites refrain from displaying a physician's rating until the sample size is sufficiently large (for example, at least 15 patient reviews). However, hospitals and health systems may be able to provide better information for patients by publishing the internal ratings of their physicians.
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What OpenQuestion holds
Registered trials
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.