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Quantifying Patient Demand for Otolaryngologists in the United States
Aravind Sreeram1, Christine G Gourin1, Anirudh Saraswathula1
1Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Objective:
To assess the geographic distribution of otolaryngologists across the United States and compare it to population-level demand using Google Trends data.
Methods:
State-level otolaryngologist workforce data (AAMC), population estimates (U.S. Census Bureau), and Google Trends relative search volume (RSV) for nine otolaryngologist-related terms were integrated. A relative demand index (RDI) was calculated by normalizing search volume averages against otolaryngologist physician density. A linear regression model was used to calculate residuals to elucidate oversupplied and undersupplied states. Spearman correlation was used to assess associations between otolaryngologist density and RSV.
Results:
Among all 50 U.S. states + Washington, DC, 10,135 otolaryngologists were identified with a median density of 0.304 per 10,000 residents (SD = 0.088). The average RSV for otolaryngologist-related terms ranged from 28.3 (North Dakota) to 72.9 (Mississippi). The highest RDI scores were observed in Mississippi (100.0), Nevada (89.1), and Oklahoma (88.6), indicating high demand relative to supply. Conversely, Washington, DC (0), North Dakota (5.2), and Oregon (10.2) exhibited the lowest RDI. Correlation analysis revealed no statistically significant association between physician density and RSV (ρ = -0.16, 95% CI, -0.41 to 0.10). Top oversupplied states included South Dakota, Wisconsin, Minnesota, Oregon, and North Dakota, while top undersupplied states included Mississippi, New Jersey, Wyoming, Connecticut, and Louisiana.
Conclusions:
Substantial geographic disparities exist between otolaryngologist availability and public digital interest, with certain states demonstrating disproportionately high demand and low supply. These findings underscore the need for data-driven workforce planning and support the utility of digital tools in identifying underserved regions.
Level Of Evidence:
N/A.

