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      Public Awareness, Usage, and Predictors for the Use of Doctor Rating Websites: Cross-Sectional Study in England

      research-article
      , BSc (Hons), PhD 1 , , , BA (Hons), PhD 2 , , BSc (Hons), PhD 3 , , MA (Hons), FRSA 3
      (Reviewer), (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      online reviews, Physician quality, primary care, Internet, quality patient empowerment, quality transparency, public reporting

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          Abstract

          Background

          With the advent and popularity of social media and consumer rating websites, as well as the emergence of the digitally engaged patient, there has been an increased interest in doctor rating websites or online patient feedback websites, both inside and outside academia. However, there is very little known about how the public across England views such rating websites as a mode to give patient experience feedback.

          Objective

          The aim of the overall study was to measure and understand public awareness, usage, and attitudes towards doctor rating websites as a mode to give experiential feedback about GPs in general practice in England. This paper reports on the findings of one of the aims of the study, which was to measure public awareness, current usage and future consideration of usage of online patient feedback websites, within the context of other feedback methods, This could allow the value of online patient feedback websites to be determined from the patients’ perspective.

          Methods

          A mixed methods population questionnaire was designed, validated and implemented face-to-face using a cross-sectional design with a representative sample of the public (n=844) in England. The results of the questionnaire were analyzed using chi-square tests, binomial logistic regressions, and content analysis. The qualitative results will be reported elsewhere.

          Results

          Public awareness of online patient feedback websites as a channel to leave experiential feedback about GPs was found to be low at 15.2% (128/844). However, usage and future consideration to use online patient feedback websites were found to be extremely low, with current patient usage at just 0.4% (3/844), and patient intention to use online patient feedback in the future at 17.8% (150/844). Furthermore, only 4.0-5.0% of those who would consider leaving feedback about a GP in the future selected doctor rating websites as their most preferred method; more than half of patients said they would consider leaving feedback about GPs using another method, but not using an online patient feedback website.

          Conclusions

          The findings suggest that online patient feedback websites may not be an effective channel for collecting feedback on patient experience in general practice. Feedback on online patient feedback websites is not likely to be representative of the patient experience in the near future, challenging the use of online patient feedback not just as a mode for collecting patient experience data, but for patient choice and monitoring too. We recommend the National Health Service channels its investment and resources towards providing more direct and private feedback methods in general practice (such as opportunities for face-to-face feedback, email-based feedback, and web-based private feedback forms), as these are currently much more likely to be used by the majority of patients in England.

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          Most cited references26

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          What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM

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            Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

            Background There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. Objective We attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care. Methods We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. Results There was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). Conclusions The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.
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              The relationship between commercial website ratings and traditional hospital performance measures in the USA.

              Our goal was to compare hospital scores from the most widely used commercial website in the USA to hospital scores from more systematic measures of patient experience and outcomes, and to assess what drives variation in the commercial website scores.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                July 2018
                25 July 2018
                : 20
                : 7
                : e243
                Affiliations
                [1] 1 School of Health and Society University of Salford Manchester United Kingdom
                [2] 2 Loughborough Design School Loughborough University Loughborough United Kingdom
                [3] 3 Warwick Manufacturing Group University of Warwick Coventry United Kingdom
                Author notes
                Corresponding Author: Salma Patel s.patel48@ 123456salford.ac.uk
                Author information
                http://orcid.org/0000-0002-6058-8382
                http://orcid.org/0000-0001-9453-0667
                http://orcid.org/0000-0001-5720-5005
                http://orcid.org/0000-0003-1407-5823
                Article
                v20i7e243
                10.2196/jmir.9523
                6083046
                30045831
                8fbbd8a6-10d2-471d-971e-ae08db3c8c8e
                ©Salma Patel, Rebecca Cain, Kevin Neailey, Lucy Hooberman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.07.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 20 January 2018
                : 10 March 2018
                : 24 April 2018
                : 5 June 2018
                Categories
                Original Paper
                Original Paper

                Medicine
                online reviews,physician quality,primary care,internet,quality patient empowerment,quality transparency,public reporting

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