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      Exploring Patients’ Views Toward Giving Web-Based Feedback and Ratings to General Practitioners in England: A Qualitative Descriptive Study

      research-article
      , BSc (Hons) 1 , , , BA (Hons), PhD 1 , , BSc (Hons),PhD 1 , , MA (Hons) Oxon,FRSA 1
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      Web-based reviews, physician quality, primary care, Internet, quality patient empowerment, quality transparency, public reporting

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          Abstract

          Background

          Patient feedback websites or doctor rating websites are increasingly being used by patients to give feedback about their health care experiences. There is little known about why patients in England may give Web-based feedback and what may motivate or dissuade them from giving Web-based feedback.

          Objective

          The aim of this study was to explore patients’ views toward giving Web-based feedback and ratings to general practitioners (GPs), within the context of other feedback methods available in primary care in England, and in particular, paper-based feedback cards.

          Methods

          A descriptive exploratory qualitative approach using face-to-face semistructured interviews was used in this study. Purposive sampling was used to recruit 18 participants from different age groups in London and Coventry. Interviews were transcribed verbatim and analyzed using applied thematic analysis.

          Results

          Half of the participants in this study were not aware of the opportunity to leave feedback for GPs, and there was limited awareness about the methods available to leave feedback for a GP. The majority of participants were not convinced that formal patient feedback was needed by GPs or would be used by GPs for improvement, regardless of whether they gave it via a website or on paper. Some participants said or suggested that they may leave feedback on a website rather than on a paper-based feedback card for several reasons: because of the ability and ease of giving it remotely; because it would be shared with the public; and because it would be taken more seriously by GPs. Others, however, suggested that they would not use a website to leave feedback for the opposite reasons: because of accessibility issues; privacy and security concerns; and because they felt feedback left on a website may be ignored.

          Conclusions

          Patient feedback and rating websites as they currently are will not replace other mechanisms for patients in England to leave feedback for a GP. Rather, they may motivate a small number of patients who have more altruistic motives or wish to place collective pressure on a GP to give Web-based feedback. If the National Health Service or GP practices want more patients to leave Web-based feedback, we suggest they first make patients aware that they can leave anonymous feedback securely on a website for a GP. They can then convince them that their feedback is needed and wanted by GPs for improvement, and that the reviews they leave on the website will be of benefit to other patients to decide which GP to see or which GP practice to join.

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          Most cited references58

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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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            Measuring patient experience: concepts and methods.

            Providing a good patient experience is a key part of providing high-quality medical care. This paper explains why patient experience is important in its own right, and its relationship to other domains of quality. We describe methods of measuring patient experience, including issues relating to validity, reliability and response bias. Differences in reported patient experience may sometimes reflect differences in expectations of different population groups and we describe the arguments for and against adjusting patient experience data for population characteristics. As with other quality improvement strategies, feeding back patient experience data on its own is unlikely to improve quality: sustained and multiple interventions are usually required to deliver sustained improvements in care.
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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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                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
                August 2016
                05 August 2016
                : 18
                : 8
                : e217
                Affiliations
                [1] 1WMG CoventryUnited Kingdom
                Author notes
                Corresponding Author: Salma Patel salma.patel@ 123456warwick.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
                v18i8e217
                10.2196/jmir.5865
                4992166
                27496366
                a5ebcaaa-cbe7-499c-90e4-f63bc16557d1
                ©Salma Patel, Rebecca Cain, Kevin Neailey, Lucy Hooberman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.08.2016.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), 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
                : 28 April 2016
                : 19 May 2016
                : 23 June 2016
                : 11 July 2016
                Categories
                Original Paper
                Original Paper

                Medicine
                web-based reviews,physician quality,primary care,internet,quality patient empowerment,quality transparency,public reporting

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