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Reputation score has been shown to be the strongest predictor of ranking in the US News & World Reports (USNWR) Best Hospitals report. However, the extent to which physicians participate in the underlying USNWR reputation survey is not well‐characterized. We conducted a retrospective cross‐sectional study of USNWR public methodology reports from 2015 to 2023 to characterize trends in physician response rates by specialty, region, and Doximity membership. Overall response rates declined between 2015 (24.0%) and 2023 (8.9%). In 2023, rates ranged from 4.7% (psychiatry) to 13.9% (otolaryngology). Otolaryngology had the highest response rate among all specialties between 2017 and 2023. Within otolaryngology, both response rates (25.0% to 13.9%) and count (2106 to 1724 physicians) declined between 2015 and 2023. Among Doximity members, response rates were consistently higher for otolaryngologists in the Northeast and Midwest compared to other regions. Though hospital rankings often influence where patients seek care, our findings suggest USNWR reputation scores may not be reliable or representative. [ABSTRACT FROM AUTHOR]

Background: Clinicians are increasingly adopting telemedicine in an effort to expand patient access and efficiently deliver care. The degree of health disparities among patients receiving otolaryngologic telemedical care is unclear. Aims: We performed a retrospective cross-sectional study to explore disparities in telemedicine delivery. Methods: We evaluated otolaryngology clinical visits from January 2019 to November 2022. We obtained patient demographics and visit characteristics (e.g., subspecialty, telemedicine vs in-person). Our primary outcome was demographic characteristics of otolaryngology patients who received telemedicine vs in-person care during the study timeframe. Results: A total of 231,384 otolaryngology clinical visits were reviewed, of which 26,895 (11.6%) were telemedicine visits. Rhinology (36.5%) and facial plastics (28.4%) subspecialties performed the most telemedicine visits. On multivariate analysis, individuals who identified as Asian, non-English speaking, and with Medicare insurance were statistically significantly less likely to use telemedicine than in-person services. Conclusion: Our findings suggest that expanding telemedicine care may not improve access for all populations, and socioeconomic factors are important considerations to ensure patients are receiving equally accessible care. Futures studies are warranted to understand how these disparities may impact health outcomes and patient satisfaction with care. [ABSTRACT FROM AUTHOR]

Objective: Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non‐ML techniques for otolaryngology–head and neck surgery cases. Methods: Deidentified patient information from otolaryngology surgical cases at 1 academic institution were reviewed from 2016 to 2020. Variables collected included patient, surgeon, procedure, and facility data known preoperatively so as to capture all realistic contributors. Available case data were divided into a training and testing data set. Several ML algorithms were evaluated based on best performance of predicted case duration when compared to actual case duration. Performance of all models was compared by the average root mean squared error and mean absolute error (MAE). Results: In total, 50,888 otolaryngology surgical cases were evaluated with an average case duration of 98.3 ± 86.9 minutes. Most cases were general otolaryngology (n = 16,620). Case features closely associated with OR duration included procedure performed, surgeon, subspecialty of case, and postoperative destination of the patient. The best‐performing ML models were CatBoost and XGBoost, which reduced operative time MAE by 9.6 minutes and 8.5 minutes compared to current methods, respectively. Discussion: The incorporation of other easily identifiable features beyond procedure performed and surgeon meaningfully improved our operative duration prediction accuracy. CatBoost provided the best‐performing ML model. Implications for Practice: ML algorithms to predict OR case time duration in otolaryngology can improve case duration accuracy and result in financial benefit. [ABSTRACT FROM AUTHOR]

This article examines the representativeness of the outcome and volume measures used by U.S. News & World Report (USNWR) in their rankings of otolaryngology departments. The study found that the USNWR volume estimate accounted for only 10% of all adult admissions and outpatient procedures at an academic otolaryngology department, and did not adequately represent the overall case mix or caseload. The analysis also revealed significant differences between USNWR-eligible and ineligible admissions in terms of subspecialty and diagnosis. The findings raise concerns about the validity of USNWR's methods and suggest the need for incorporating quality metrics that better reflect the diversity of patients seeking otolaryngologic care. [Extracted from the article]