Study Design: Retrospective cohort. Objective: Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. Methods: We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. Results: The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). Conclusions: We show that the XLNet model can be successfully applied to orthopedic surgeon’s operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

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: 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.

» 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.

The current standard of care for patients with end-stage renal disease (ERSD) is a kidney transplant or dialysis when a donor organ isnot available. The growing gap between patients who require a kidney transplant and the availability of donor organs as well as thenegative effects of long-term dialysis, such as infection, limited mobility, and risk of cancer development, drive the impetus to developalternative renal replacement technology. The goal of this review is to assess the potential of two of the most recent innovations inkidney transplant technology—the implantable bioartificial kidney (BAK) and kidney regeneration technology—in addressing the aforementionedproblems related to kidney replacement for patients with ERSD. Both innovations are fully implantable, autologous, personalizedwith patient cells, and can replace all aspects of kidney function. Not only do these new innovations have the potential toimprove the possibility of transplantation for more patients, they also have potential to improve the outcome of transplantation or dialysis-related renal cancer diagnosis. A major limitation of the current technology is that both implantable BAK and kidney regenerationtechnology are still in preclinical stages, and thus their potential effects cannot be comprehensively generalized to human patients.