Literature DB >> 22207014

Feasibility of real-time satisfaction surveys through automated analysis of patients' unstructured comments and sentiments.

Farrokh Alemi1, Manabu Torii, Laura Clementz, David C Aron.   

Abstract

This article shows how sentiment analysis (an artificial intelligence procedure that classifies opinions expressed within the text) can be used to design real-time satisfaction surveys. To improve participation, real-time surveys must be radically short. The shortest possible survey is a comment card. Patients' comments can be found online at sites organized for rating clinical care, within e-mails, in hospital complaint registries, or through simplified satisfaction surveys such as "Minute Survey." Sentiment analysis uses patterns among words to classify a comment into a complaint, or praise. It further classifies complaints into specific reasons for dissatisfaction, similar to broad categories found in longer surveys such as Consumer Assessment of Healthcare Providers and Systems. In this manner, sentiment analysis allows one to re-create responses to longer satisfaction surveys from a list of comments. To demonstrate, this article provides an analysis of sentiments expressed in 995 online comments made at the RateMDs.com Web site. We focused on pediatrician and obstetrician/gynecologist physicians in District of Columbia, Maryland, and Virginia. We were able to classify patients' reasons for dissatisfaction and the analysis provided information on how practices can improve their care. This article reports the accuracy of classifications of comments. Accuracy will improve as the number of comments received increases. In addition, we ranked physicians using the concept of time-to-next complaint. A time-between control chart was used to assess whether time-to-next complaint exceeded historical patterns and therefore suggested a departure from norms. These findings suggest that (1) patients' comments are easily available, (2) sentiment analysis can classify these comments into complaints/praise, and (3) time-to-next complaint can turn these classifications into numerical benchmarks that can trace impact of improvements over time. The procedures described in the article show that real-time satisfaction surveys are possible.

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Mesh:

Year:  2012        PMID: 22207014     DOI: 10.1097/QMH.0b013e3182417fc4

Source DB:  PubMed          Journal:  Qual Manag Health Care        ISSN: 1063-8628            Impact factor:   0.926


  21 in total

1.  What Words Convey: The Potential for Patient Narratives to Inform Quality Improvement.

Authors:  Rachel Grob; Mark Schlesinger; Lacey Rose Barre; Naomi Bardach; Tara Lagu; Dale Shaller; Andrew M Parker; Steven C Martino; Melissa L Finucane; Jennifer L Cerully; Alina Palimaru
Journal:  Milbank Q       Date:  2019-03       Impact factor: 4.911

2.  A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews.

Authors:  Byron C Wallace; Michael J Paul; Urmimala Sarkar; Thomas A Trikalinos; Mark Dredze
Journal:  J Am Med Inform Assoc       Date:  2014-06-10       Impact factor: 4.497

3.  Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).

Authors:  Marieke M van Buchem; Olaf M Neve; Ilse M J Kant; Ewout W Steyerberg; Hileen Boosman; Erik F Hensen
Journal:  BMC Med Inform Decis Mak       Date:  2022-07-15       Impact factor: 3.298

4.  Online Ratings and Perceptions of Pediatric Otolaryngologists.

Authors:  Janice T Chua; Emily Nguyen; Adwight Risbud; Sina Soltanzadeh-Zarandi; Ariel Lee; Shahrnaz Jamshidi; Soha Bayginejad; Mehdi Abouzari
Journal:  Laryngoscope       Date:  2021-02-24       Impact factor: 2.970

5.  Collecting and Analyzing Patient Experiences of Health Care From Social Media.

Authors:  Majid Rastegar-Mojarad; Zhan Ye; Daniel Wall; Narayana Murali; Simon Lin
Journal:  JMIR Res Protoc       Date:  2015-07-02

6.  Web-based textual analysis of free-text patient experience comments from a survey in primary care.

Authors:  Inocencio Daniel Maramba; Antoinette Davey; Marc N Elliott; Martin Roberts; Martin Roland; Finlay Brown; Jenni Burt; Olga Boiko; John Campbell
Journal:  JMIR Med Inform       Date:  2015-05-06

Review 7.  Eight questions about physician-rating websites: a systematic review.

Authors:  Martin Emmert; Uwe Sander; Frank Pisch
Journal:  J Med Internet Res       Date:  2013-02-01       Impact factor: 5.428

8.  An analysis of online evaluations on a physician rating website: evidence from a German public reporting instrument.

Authors:  Martin Emmert; Florian Meier
Journal:  J Med Internet Res       Date:  2013-08-06       Impact factor: 5.428

9.  Physician choice making and characteristics associated with using physician-rating websites: cross-sectional study.

Authors:  Martin Emmert; Florian Meier; Frank Pisch; Uwe Sander
Journal:  J Med Internet Res       Date:  2013-08-28       Impact factor: 5.428

10.  Use of sentiment analysis for capturing patient experience from free-text comments posted online.

Authors:  Felix Greaves; Daniel Ramirez-Cano; Christopher Millett; Ara Darzi; Liam Donaldson
Journal:  J Med Internet Res       Date:  2013-11-01       Impact factor: 5.428

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