A Comparison of Online Physician Ratings and Internal Patient-Submitted Ratings from a Large Healthcare System

Okike, Kanu; Uhr, Natalie R; Shin, Sherry Y M; Xie, Kristal; Kim, Chong Y.; Funahashi, Tadashi T.; Kanter, Michael H.

doi:10.1007/s11606-019-05265-3

Cited by 8 publications

(7 citation statements)

References 28 publications

(40 reference statements)

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“…Chen et al (2018) found significant weaker correlations ( r between .18 and .27), whereas Rotman et al (2019) found a stronger correlation ( r between .77 and .79). Consistent with other findings, Okike et al (2019) found that the strength of the correlation between OPRs and patient experience scores was influenced by the number of reviews included in the OPR measure. When the authors increased the minimum number of reviews from 5 to 15, the correlation increased from r = .23 to r = .42.…”

Section: Resultssupporting

confidence: 89%

“…Another interesting finding was based on whether the studies accounted for physician specialty. While Rotman et al (2019) focused on specialists, other studies included both specialists and generalists (Chen et al, 2018; Daskivich et al, 2017; Okike et al, 2019). Although Okike et al’s (2019) study included both generalists and specialists, their analysis did not examine whether correlations varied by specialty.…”

Section: Resultsmentioning

confidence: 99%

“…While Rotman et al (2019) focused on specialists, other studies included both specialists and generalists (Chen et al, 2018; Daskivich et al, 2017; Okike et al, 2019). Although Okike et al’s (2019) study included both generalists and specialists, their analysis did not examine whether correlations varied by specialty. In contrast, Chen et al (2018), performing partial correlational analysis, found that the strength of the correlation between OPRs and patient experience increased after they adjusted for physician specialty.…”

Section: Resultsmentioning

confidence: 99%

“…Our analysis indicates that the impact of sample size on the association between OPRs and outcome measures should be further examined. First, a few studies that directly examined the sample size of the OPRs comprising the rating for each observation found that the strength of the association between OPRs and outcome measures increased as the minimum number of reviews increased (Bardach et al, 2012; Okike et al, 2019; Ranard et al, 2016). The study that found the strongest association between OPRs and clinical quality also had the largest sample size with respect to OPRs per provider ( n = 50; Rotman, 2019).…”

Section: Discussionmentioning

confidence: 99%

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Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Placona

Rathert

2021

Med Care Res Rev

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Although online patient reviews (OPRs) are ubiquitous, there has been debate about whether OPRs are associated with measures of health care outcomes. The majority of patients search for online information about a hospital or provider before scheduling an encounter, therefore, the extent to which OPRs are valid information is important. We conducted a systematic review of the literature to examine the extent to which OPRs are associated with measures of patient experience, clinical quality, and other outcomes. We searched Medline, CINAHL, Web of Science, and PsycInfo, which yielded 32 studies. There were consistent positive correlations between OPRs and patient experience at both the organization and individual provider levels of analysis. However, associations between OPRs and quality measures were mixed. Organizational level OPRs may be more reliable than individual provider OPRs. In addition, the strength of association could be affected by the type of encounter setting, specialty, and specific measures.

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Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Placona

Rathert

2021

Med Care Res Rev

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“…Consequently, reviews and online ratings in the healthcare industry are less useful and effective compared to those of other industries [28,29]. However, some studies, on the contrary, have revealed a direct correlation between online user ratings and clinical outcomes [30][31][32][33].…”

mentioning

confidence: 99%

Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews

Kalabikhina,

Moshkin,

Kolotusha

et al. 2024

Mathematics

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Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other.

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Online ratings and narrative comments of American Head and Neck Society surgeons

Kim,

Tawk,

Kim

et al. 2024

Head & Neck

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BackgroundWe analyzed online rating scores and comments of head and neck surgeons to understand factors that contribute to higher ratings.MethodsNumerical ratings and comments for American Head and Neck Society physicians were extracted from Healthgrades, Vitals, RateMDs, and Yelp, with narrative comments categorized based on content. Physician practice location, education, and residency training were also compiled.ResultsPatient ratings were significantly higher with supportive staff and affable physician demeanor but showed significant drops with longer wait times and difficulties scheduling appointments or follow‐ups. Physician education and postgraduate training did not significantly affect ratings.ConclusionOnline ratings and comments correlated to modifiable factors in clinical practice and may be informative in understanding patient needs.

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A Comparison of Online Physician Ratings and Internal Patient-Submitted Ratings from a Large Healthcare System

Cited by 8 publications

References 28 publications

Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews

Online ratings and narrative comments of American Head and Neck Society surgeons

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