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Sentiment Analysis of Online Patient-Written Reviews of Vascular Surgeons

Published:August 22, 2022DOI:https://doi.org/10.1016/j.avsg.2022.07.016

      Background

      Online patient reviews influence a patient's choice of a vascular surgeon. The aim of this study is to examine underlying factors that contribute to positive and negative patient reviews by leveraging sentiment analysis and machine learning methods.

      Methods

      The Society of Vascular Surgeons publicly accessible member directory was queried and cross-referenced with a popular patient-maintained physician review website, healthgrades.com. Sentiment analysis and machine learning methods were used to analyze several parameters. Demographics (gender, age, and state of practice), star rating (of 5 stars), and written reviews were obtained for corresponding vascular surgeons. A sentiment analysis model was applied to patient-written reviews and validated against the star ratings. Student's t-test or one-way analysis of variance assessed demographic relationships with reviews. Word frequency assessments and multivariable logistic regression analyses were conducted to identify common and determinative components of written reviews.

      Results

      A total of 1,799 vascular surgeons had public profiles with reviews. Female gender of surgeon was associated with lower star ratings (male = 4.19, female = 3.95, P < 0.01) and average sentiment score (male = 0.50, female = 0.40, P < 0.01). Younger physician age was associated with higher star rating (P = 0.02) but not average sentiment score (P = 0.12). In the Best reviews, the most commonly used one-words were Care (N = 999), Caring (N = 767), and Kind (N = 479), while the most commonly used two-word pairs were Saved/Life (N = 189), Feel/Comfortable (N = 106), and Kind/Caring (N = 104). For the Worst reviews, the most commonly used one-words were Pain (N = 254) and Rude (N = 148), while the most commonly used two-word pairs were No/One (N = 27), Waste/Time (N = 25), and Severe/Pain (N = 18). In a multiple logistic regression, satisfactory reviews were associated with words such as Confident (odds ratio [OR] = 8.93), Pain-free (OR = 4.72), Listens (OR = 2.55), and Bedside Manner (OR = 1.70), while unsatisfactory reviews were associated with words such as Rude (OR = 0.01), Arrogant (OR = 0.09), Infection (OR = 0.20), and Wait (OR = 0.48).

      Conclusions

      Female surgeons received significantly worse reviews and younger surgeons tended to receive better reviews. The positivity and negativity of reviews were largely related to words associated with the patient–doctor experience and pain. Vascular surgeons should focus on these 2 areas to improve patient experiences and their own reviews.
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      References

        • Shemirani N.L.
        • Castrillon J.
        Negative and positive online patient reviews of physicians-1 vs 5 stars.
        JAMA Facial Plast Surg. 2017; 19: 435-436
        • Furnas H.J.
        • Korman J.M.
        • Canales F.L.
        • et al.
        Patient reviews: yelp, google, healthgrades, vitals, and RealSelf.
        Plast Reconstr Surg. 2020; 146: 1419-1431
        • Penaflorida II, R.
        Doctor review sites where you should have a profile. Reviewtrackers.
        • Han X.
        • Qu J.
        • Zhang T.
        Exploring the impact of review valence, disease risk, and trust on patient choice based on online physician reviews.
        Telematics Inform. 2019; 45: 1-10
        • Yan Q.
        • Jensen K.J.
        • Thomas R.
        • et al.
        Digital footprint of academic vascular surgeons in the southern United States on physician rating websites: cross-sectional evaluation study.
        JMIR Cardio. 2021; 5: e22975
        • Murphy G.P.
        • Radadia K.D.
        • Breyer B.N.
        Online physician reviews: is there a place for them?.
        Risk Manag Healthc Policy. 2019; 12: 85-89
        • Abdullah M.S.
        • Bilello J.S.
        • Fankhauser G.
        Digital presence of vascular surgeons.
        J Am Coll Surg. 2019; 229: S326
        • Emmert M.
        • Meier F.
        • Pisch F.
        • et al.
        Physician choice making and characteristics associated with using physician-rating websites: cross-sectional study.
        J Med Internet Res. 2013; 15: e187
        • Phair J.
        • Dalmia V.
        • Sanon O.
        • et al.
        The current state of vascular surgery presence and educational content in Google and YouTube internet search results.
        J Vasc Surg. 2021; 74: 616-624.e6
        • Wanken Z.J.
        • Rode J.B.
        • Bessen S.Y.
        • et al.
        Online ratings for vascular interventional proceduralists vary by physician specialty.
        Ann Vasc Surg. 2021; 70: 27-35
        • Tang J.E.
        • Arvind V.
        • White C.A.
        • et al.
        What are patients saying about you online? A sentiment analysis of online written reviews on Scoliosis Research Society surgeons.
        Spine Deform. 2021; 10: 301-306
        • Hutto C.
        • Gilbert E.
        VADER: a parsimonious rule-based model for sentiment analysis of social media text.
        Proc Int AAAI Conf Web Social Media. 2014; 8: 216-225
        • Roe C.
        • Lowe M.
        • Williams B.
        • et al.
        Public perception of SARS-CoV-2 vaccinations on social media: questionnaire and sentiment analysis.
        Int J Environ Res Public Health. 2021; 18: 1-21
        • Turner J.
        • Kantardzic M.
        • Vickers-Smith R.
        Infodemiological examination of personal and commercial tweets about cannabidiol: term and sentiment analysis.
        J Med Internet Res. 2021; 23: e27307
        • Alam K.N.
        • Khan M.S.
        • Dhruba A.R.
        • et al.
        Deep learning-based sentiment analysis of COVID-19 vaccination responses from twitter data.
        Comput Math Methods Med. 2021; 2021: 1-15
        • Kirkpatrick W.
        • Abboudi J.
        • Kim N.
        • et al.
        An assessment of online reviews of hand surgeons.
        Arch Bone Jt Surg. 2017; 5: 139-144
        • Humphries M.D.
        • Mikityuk A.
        • Harris L.
        • et al.
        Representation of women in vascular surgery science and societies.
        J Vasc Surg. 2021; 74: 15S-20S
        • Dorsey C.
        • Ross E.
        • Appah-Sampong A.
        • et al.
        Update on workforce diversity in vascular surgery.
        J Vasc Surg. 2021; 74: 5-11.e1
        • Silva F.C.S.
        • Cerqueira M.
        • Mercês M.C.D.
        • et al.
        Strengths and barriers for women in vascular surgery: the Brazilian perspective.
        Eur J Vasc Endovasc Surg. 2020; 60: 165-166
        • Smeds M.R.
        • Aulivola B.
        Gender disparity and sexual harassment in vascular surgery practices.
        J Vasc Surg. 2020; 72: 692-699
        • Nukala M.
        • Freedman-Weiss M.
        • Yoo P.
        • et al.
        Sexual harassment in vascular surgery training programs.
        Ann Vasc Surg. 2020; 62: 92-97

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