Study Design A Sentiment Analysis of online reviews of spine surgeons. Objectives Physician review websites have significant impact on a patient’s provider selection. Written reviews are subjective, but sentiment analysis through machine learning can quantitatively analyze these reviews. This study analyzes online written reviews of spine surgeons and reports biases associated with demographic factors and trends in words utilized. Methods Online written and star-reviews of spine surgeons were obtained from healthgrades.com . A sentiment analysis package was used to analyze the written reviews. The relationship of demographic variables to these scores was analyzed with t-tests and word and bigram frequency analyses were performed. Additionally, a multiple regression analysis was performed on key terms. Results 8357 reviews of 480 surgeons were analyzed. There was a significant difference between the means of sentiment analysis scores and star scores for both gender and age. Younger, male surgeons were rated more highly on average ( P < .01). Word frequency analysis indicated that behavioral factors and pain were the main contributing factors to both the best and worst reviewed surgeons. Additionally, several clinically relevant words, when included in a review, affected the odds of a positive review. Conclusions The best reviews laud surgeons for their ability to manage pain and for exhibiting positive bedside manner. However, the worst reviews primarily focus on pain and its management, as exhibited by the frequency and multivariate analysis. Pain is a clear contributing factor to reviews, thus emphasizing the importance of establishing proper pain expectations prior to any intervention.

Background: Physician review websites have influence on a patient’s selection of a provider. Written reviews are subjective and difficult to quantitatively analyze. Sentiment analysis of writing can quantitatively assess surgeon reviews to provide actionable feedback for surgeons to improve practice. The objective of this study is to quantitatively analyze large subset of written reviews of hand surgeons using sentiment analysis and report unbiased trends in words used to describe the reviewed surgeons and biases associated with surgeon demographic factors. Methods: Online written and star-rating reviews of hand surgeons were obtained from healthgrades.com and webmd.com . A sentiment analysis package was used to calculate compound scores of all reviews. Mann-Whitney U tests were performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was also performed. Results: A total of 786 hand surgeons’ reviews were analyzed. Analysis showed a significant relationship between the sentiment scores and overall average star-rated reviews ( r2 = 0.604, P ≤ .01). There was no significant difference in review sentiment by provider sex; however, surgeons aged 50 years and younger had more positive reviews than older ( P < .01). The most frequently used bigrams used to describe top-rated surgeons were associated with good bedside manner and efficient pain management, whereas those with the worst reviews are often characterized as rude and unable to relieve pain. Conclusions: This study provides insight into both demographic and behavioral factors contributing to positive reviews and reinforces the importance of pain expectation management.

Study Design Retrospective database study. Objectives The goal of this study was to assess the influence of weekend admission on patients undergoing elective thoracolumbar spinal fusion by investigating hospital readmission outcomes and analyzing differences in demographics, comorbidities, and postoperative factors. Methods The 2016-2018 Nationwide Readmission Database was used to identify adult patients who underwent elective thoracolumbar spinal fusion. The sample was divided into weekday and weekend admission patients. Demographics, comorbidities, complications, and discharge status data were compiled. The primary outcomes were 30-day and 90-day readmission. Univariate logistic regression analyzed the relationship between weekday or weekend admission and 30- or 90-day readmission, and multivariate regression determined the impact of covariates. Results 177,847 patients were identified in total, with 176,842 in the weekday cohort and 1005 in the weekend cohort. Multivariate regression analysis found that 30-day readmissions were significantly greater for the weekend cohort after adjusting for sex, age, Medicare or Medicaid status, and comorbidity status (OR 2.00, 95% CI: 1.60-2.48; P < .001), and 90-day readmissions were also greater for the weekend cohort after adjustment (OR 2.01, 95% CI: 1.68-2.40, P < .001). Conclusions Patients undergoing elective thoracolumbar spinal fusion surgery who are initially admitted on weekends are more likely to experience hospital readmission. These patients have increased incidence of deep vein thrombosis, postoperative infection, and non-routine discharge status. These factors are potential areas of focus for reducing the impact of the “weekend effect” and improving outcomes for elective thoracolumbar spinal fusion.

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Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

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Background Tobacco carcinogens have adverse effects on bone health and are associated with inferior outcomes following orthopedic procedures. The purpose of this study was to assess the impact tobacco use has on readmission and complication rates following shoulder arthroplasty. Methods The 2016–2018 National Readmissions Database was queried to identify patients who underwent anatomical, reverse, and hemi-shoulder arthroplasty. ICD-10 codes Z72.0 ×  (tobacco use disorder) and F17.2 ×  (nicotine dependence) were used to define “tobacco-users.” Demographic, 30-/90-day readmission, surgical complication, and medical complication data were collected. Inferential statistics were used to analyze complications for both the cohort as a whole and for each procedure separately (i.e. anatomical, reverse, and hemiarthroplasty). Results 164,527 patients were identified (92% nontobacco users). Tobacco users necessitated replacement seven years sooner than nonusers ( p < 0.01) and were more likely to be male (52% vs. 43%; p < 0.01). Univariate analysis showed that tobacco users had higher rates of readmission, revisions, shoulder complications, and medical complications overall. In the multivariate analysis for the entire cohort, readmission, revision, and complication rates did not differ based on tobacco usage; however, smokers who underwent reverse shoulder arthroplasty in particular were found to have higher 90-day readmission, dislocation, and prosthetic complication rates compared to nonsmokers. Conclusion Comparatively, tobacco users required surgical correction earlier in life and had higher rates of readmission, revision, and complications in the short term following their shoulder replacement. However, when controlling for tobacco usage as an independent predictor of adverse outcomes, these aforementioned findings were lost for the cohort as a whole. Overall, these findings indicate that shoulder replacement in general is a viable treatment option regardless of patient tobacco usage at short-term follow-up, but this conclusion may vary depending on the replacement type used.