We gathered data from publicly available sources. The state population totals from 2020 to 2022 were obtained from the United States Census Bureau [3]. The location of every accredited orthopedic surgery residency program in the United States was collected from the Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory [4]. The data of the current physician supply, number of undergraduate medical education students, and number of graduate medical education (GME) residents and fellows in the United States were gathered from the most up-to-date 2021 AAMC State Physician Workforce Data Report [5]. Lastly, Google Trends was used to search for data of “Orthopedic surgeons” from 2004 to 2023 to determine the demand for orthopedic surgeons in the United States [6].

This study was conducted retrospectively and did not involve the use of any patient health record data. Therefore, it did not require assessment or approval by an institutional ethics review board.

To quantify the relative demand for orthopedic surgeons across the country, we first calculated the relative search volume (RSV) in each state by using the search volume data collected from Google. Google Trends reported the data on a state-by-state basis, so we divided the frequency of specific searches for “Orthopedic surgeons” in every state by the monthly total number of all other searches performed in the state and then obtained the average value across the 2004–2023 time span. This number was then normalized on a scale from 0 to 100 to obtain the RSV, a metric that allowed us to compare search volumes across states with different populations. The higher the normalized RSV, the higher the interest in the topic.

The RSV was then used to calculate the RDI, our quantitative metric for relative orthopedic surgeon demand. We divided the number of practicing orthopedic surgeons per state, obtained from the 2021 AAMC State Physician Workforce Data Report, by the United States census state population to calculate the approximate concentration of orthopedic surgeons per 10,000 individuals. The concentration of orthopedic surgeons per 10,000 individuals was normalized on a scale from 0 to 100, where 100 represented the greatest concentration. Normalization was performed through the minimum–maximum method. For each value, the difference between the minimum and maximum numbers in the dataset was divided by the range of the dataset. We were able to use this method to enhance readability and preserve relationships between the original values because no outliers were identified in our dataset based on interquartile ranges. All the calculations are shown below.

The RSV was then divided by the concentration of orthopedic surgeons to obtain the RDI, also normalized on a scale from 0 to 100. The normalized RDI measures the relative demand for orthopedic surgeons by incorporating both their supply and demand; thus, a state with a normalized RDI of 100 has the highest relative demand and a state with a normalized RDI of 0 has the lowest demand. As a result, it also serves as a marker of supply; the states with a higher normalized RDI have a relative undersupply of orthopedic surgeons whereas the states with a lower normalized RDI have a relative oversupply.

For example, if State A has an RSV of 0.005 (0.5% of all searches) and a surgeon concentration of 1 surgeon per 10,000 people (0.0001), the RDI is 50. In contrast, if State B has the same RSV but a higher surgeon concentration of 1 surgeon per 5000 people (0.0002), the RDI is 25. Even with identical demand, the higher RDI for State A reflects a greater relative demand due to fewer surgeons available per person.

To highlight the geographical variation observed between the supply of and demand for orthopedic surgeons, we subtracted the concentration of orthopedic surgeons per 10,000 people from the normalized RDI. This yielded the difference in interest and concentration, a metric from 0 to 100 that describes the relationship between the supply of and the demand for orthopedic surgeons. The larger the difference, the greater the disparity between the search volume of orthopedic surgeons and the orthopedic surgeon concentration in the state, and conversely, there is less of a disparity when the difference is smaller.

Correlation analysis was run using Spearman rank correlation coefficient testing, which measures the strength and direction of association between two ranked variables. The statistical significance threshold was set as P<.05. Statistical analysis was performed with R software version 4.3.3 (R Foundation for Statistical Computing).

In this study, the observed relationship between the number of orthopedic residency programs and the normalized RDI is of interest. Certain states exhibited a scarcity of orthopedic residency programs and a limited demand for orthopedic services, such as South Dakota, North Dakota, Wyoming, Montana, and Minnesota. Conversely, a discernible trend emerged wherein states with a greater demand for orthopedic services showed a proportionate increase in the concentration of residency programs. This trend is found in other specialties as well. For example, in psychiatry, Pheister et al [7] found that 84.2% of residency program directors who started or expanded programs listed the shortage of local psychiatrists as the main driver.

We found a high orthopedic surgeon concentration and relatively low normalized RDI in the Midwest. This lower normalized RDI may signify that the demand for orthopedic surgeons is met with a greater concentration of such surgeons. As a region with a smaller concentration of metropolitan areas, finding that this market was saturated was particularly intriguing due to the well-documented trend of physicians preferring to practice in more urban regions. For example, in a systematic literature review, Cyr et al [8] concluded that specialists gravitate toward urbanized areas with more potential patients to support their practice.

Our approach has been validated by Ikpeze et al [9], Blau et al [10], and Akosman et al [11]. Combining Google Trends data and the number of orthopedic surgeons and residency programs from the AAMC allowed us to interpret the geographical distribution of demand. Ikpeze et al [9] examined the relationship between the orthopedic surgery applicant pool in the United States and Internet queries for orthopedic residency. They found that searches for “orthopedic residency” and “orthopedic salary” increased dramatically from 2004 to 2015. The National Resident Matching Program database also showed a consistent yearly increase in orthopedic surgery applicants. This positive correlation between an official database and Google Trends demonstrates its validity in gauging public interest in a topic [9].

Google Trends analysis has explored other specialties and topics outside of orthopedic surgery. Blau et al [10] looked at all searches for “plastic surgery” over 12 months, and similar to our study, used RSV and surgeons-per-capita value, or “surgical concentration.” Their Google RSV divided by this concentration yielded a “surgical demand index” for each state, which is similar to and validates our study’s normalized RDI. Their “surgical demand index” was the greatest in Wyoming, Oklahoma, and Arkansas; and the smallest in Oregon, Virginia, and Connecticut [10].

Akosman et al [11] performed a similar analysis for the ophthalmology specialty; they suggested that states with a higher normalized RDI may have an unmet medical need for ophthalmic care. The highest normalized RDI was found in South Dakota, Delaware, Michigan, and Arizona. The lowest normalized RDI was in Washington DC, Hawaii, Oregon, and Montana. In this study for the orthopedic specialty, we found the lowest normalized RDI to be in the District of Columbia, Alaska, and New Hampshire.

Career factors that may have influenced the geographical distribution include the supply of advanced medical facilities, renowned orthopedic institutions, research opportunities, and access to innovative technology. Additionally, lifestyle factors, including the overall quality of life, salary, cost of living, and recreational amenities in a particular state, are influential variables as well. It is difficult to objectively explain the drive to or away from certain states. The insight our data provides regarding the demand for orthopedic services can guide new orthopedic surgeons to places in the United States where they are needed. In addition, it could also inform where to establish new residencies, as well as how existing residency programs should invest in recruiting medical students to their state. The limitations to establishing such programs include high costs and complex requirements by the Accreditation Council for Graduate Medical Education (ACGME), which can be overcome by acquiring increased GME funding and partnering with hospitals, universities, and policymakers to reduce financial burden and facilitate meeting rigorous accreditation standards.

Future work may include combining orthopedic surgeon demand with the demand for specific orthopedic procedures. This would further develop our understanding of unmet orthopedic care needs in many states. For example, Cohen et al [12] measured the national public interest in the use of platelet-rich plasma for hip and knee osteoarthritis. In an analogous study with total knee arthroplasty and total hip arthroplasty, Cohen et al [13] also suggested that this data would be useful in making resource allocation decisions in states. Trends in the public interest may inform patient counseling, shared decision-making, and directions for future clinical research.

Google was the only search engine used in this study, and it is possible that data from other search engines may yield different results, potentially affecting the generalizability of our findings. Additionally, while this study provides insights into regional trends, the absence of complementary data, such as hiring rates or job postings, limits the ability to draw definitive conclusions about the supply-demand dynamics in orthopedic surgery. The use of general search terms, such as “orthopedic surgery,” introduces potential biases, including the influence of surges of unrelated searches that may not reflect genuine patient interest. For example, during emergencies, the volume of orthopedic surgeries and searches for orthopedic services may not be as high. Our dataset does not control for this. Furthermore, online search data may not accurately represent underserved populations or individuals with limited internet access, introducing inherent demographic and geographic biases. These limitations underscore the need for integrating online search data with traditional epidemiological and workforce datasets in future studies to achieve more nuanced and representative analyses.

The supply of orthopedic surgeons varies greatly across the United States, with a higher demand for them in southern states. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively. States that were in the top quartile of residency programs (≥4 residency programs) exhibited a high demand for orthopedic surgeons. This study highlights the utility of the RDI as a scalable, data-driven tool for informing precision public health strategies. Beyond orthopedic care, the RDI framework could be adapted to other medical specialties, such as cardiology or general surgery, to identify underserved regions and guide resource allocation. For instance, the RDI can inform initiatives such as expanding residency programs, offering loan repayment or grant incentives, and deploying specialists to areas with high demand but limited supply. Additionally, if the RDI is correlated with specific procedures or conditions, such as knee surgeries in orthopedics or cardiac catheterizations in cardiology, it could be leveraged to target preventative public health initiatives. Examples include obesity reduction programs to decrease the need for joint replacements or smoking cessation campaigns to reduce cardiovascular disease prevalence. By integrating the RDI into broader public health frameworks, stakeholders can optimize care delivery, address disparities, and allocate resources more effectively across various health care settings.