Feasibility of Real-Time Satisfaction Surveys Through Automated Analysis of Patients' Unstructured Comments and Sentiments

Alemi, Farrokh; Torii, Manabu; Clementz, Laura; Aron, David C.

doi:10.1097/qmh.0b013e3182417fc4

Cited by 62 publications

(68 citation statements)

References 11 publications

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“…This appears to support the argument made by GPs in a previous study [47] as well as physician representatives [68,69] that the majority of Web-based patient feedback is extreme negative opinion. This is usually counteracted in the literature with the statement that studies (including [7,10,20,23,30,35,48,59,70-72]) in and out of the United Kingdom have found that the majority of feedback left on physician review websites is positive [73]. The findings from this study appear to contradict that and further suggest that regardless of whether patient feedback is given on a website or not, patients are much more likely to leave feedback when they have experienced an extreme negative experience.…”

Section: Discussionmentioning

confidence: 99%

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

Patel¹,

Cain²,

Neailey³

et al. 2016

J Med Internet Res

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BackgroundPatient 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.ObjectiveThe 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.MethodsA 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.ResultsHalf 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.ConclusionsPatient 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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Section: Discussionmentioning

confidence: 99%

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

Patel¹,

Cain²,

Neailey³

et al. 2016

J Med Internet Res

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“…Previous studies have found significant associations between ratings left on websites and clinical outcomes, with better rated hospitals having lower mortality and healthcare associated infection rates 14 15. Early research has demonstrated that sentiment analysis of content in patients’ comments about their care on the internet is feasible 16 17. Sentiment analysis involves taking unstructured, often free-text information, and using software to judge whether the information is broadly positive or negative.…”

Section: Social Media Healthcare Quality and Learning From Other Indmentioning

confidence: 99%

“…Sentiment analysis involves taking unstructured, often free-text information, and using software to judge whether the information is broadly positive or negative. Alemi and colleagues have called for real time patient satisfaction surveys using these analyses 16. Cambria and others believe that individual experiences could be ‘crowd validated’ by aggregating various sources of unstructured information from patients online 18…”

Section: Social Media Healthcare Quality and Learning From Other Indmentioning

confidence: 99%

Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

et al. 2013

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Recent years have seen increasing interest in patient-centred care and calls to focus on improving the patient experience. At the same time, a growing number of patients are using the internet to describe their experiences of healthcare. We believe the increasing availability of patients' accounts of their care on blogs, social networks, Twitter and hospital review sites presents an intriguing opportunity to advance the patient-centred care agenda and provide novel quality of care data. We describe this concept as a 'cloud of patient experience'. In this commentary, we outline the ways in which the collection and aggregation of patients' descriptions of their experiences on the internet could be used to detect poor clinical care. Over time, such an approach could also identify excellence and allow it to be built on. We suggest using the techniques of natural language processing and sentiment analysis to transform unstructured descriptions of patient experience on the internet into usable measures of healthcare performance. We consider the various sources of information that could be used, the limitations of the approach and discuss whether these new techniques could detect poor performance before conventional measures of healthcare quality.

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“…However, processing of text evaluation information is still considered as an accessorial state during the evaluation procedure, and its role has not yet been identified. Substantial research has been carried out regarding the text information problem, which mainly falls in the categories of text clustering (Bouguila, 2011;He, 2011), text mining (Aggarwal, 2012), integration of text information (Henning, 2009;Yin, 2008), text reasoning (Alemi, 2012;Neveol, 2011), recommending systems on the basis of text (Li, 2011), analyzing technique of text abstract (Hien, 2011), and so on. From an analysis of the existing literature, we can conclude that, on one hand, there is ample research on formatted text information in the decision-making process, and that this kind of information can be regarded as a classical form of multi-criterion decision making.…”

Section: Introductionmentioning

confidence: 99%

Group aggregation method of non-formatted text evaluation information based on linguistic label and evidence distance

Wang

Zhu

Yang

et al. 2013

Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)

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A group decision-making model for non-formatted text evaluation information is suggested. First, the problem is transformed into a particular multi-criterion decision making frame based on multi-granular linguistic label, which includes the comprehensive evaluation score and the evaluation criterion point score. Second, a weighted model for the criteria is proposed based on the minimal difference between the comprehensive evaluation score and the evaluation criterion point score for decision makers (DMs). Third, a numerical estimation model for the incomplete evaluation item is established based on the minimal evidence distance of Dempster-Shafer theory, which is maximal group consistency. In addition, a weight calculation method for the DM is suggested through the logistic consistency of the DM and the similarity among the groups. Furthermore, the score modification method for the alternative is proposed according to the weight of the criteria and the DM preferences. Finally, a case of a soft science project evaluation and selection illustrates the application process and the feasibility of the proposed method.

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Feasibility of Real-Time Satisfaction Surveys Through Automated Analysis of Patients' Unstructured Comments and Sentiments

Abstract: possible.

Cited by 62 publications

References 11 publications

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

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

Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

Group aggregation method of non-formatted text evaluation information based on linguistic label and evidence distance

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