ARRS 2022 Abstracts

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E1094. Google Trends as a Potential Tool for Predicting Radiologist Demand
Authors
  1. Christine Doepker; West Virginia University School of Medicine
  2. Haig Pakhchanian; George Washington School of Medicine and Health Sciences
  3. Rahul Raiker; West Virginia University School of Medicine
  4. Dhairya Lakhani; West Virginia University School of Medicine
  5. Jeffery Hogg; West Virginia University School of Medicine
Objective:
The objective of this study was to identify the demand for radiologists (R) and radiologists who accept Medicare (RM) per state from 2004–2019 using Google Trends, and place such demand in context with additional state data.

Materials and Methods:
The total number of R and RM per state was divided by each state’s population to achieve respective radiologist densities of each per 10,000 residents. Relative search volume (RSV) for the term “radiologist” was collected from Google Trends data from 2004–2019. The Radiologist Demand Index (RDI) was calculated by dividing each state’s RSV by the radiologist (R and RM) density for that state. To standardize values, each state’s RDI was divided by the largest RDI within the two respective groups to generate the Relative Radiologist Demand Index (RRDI). Imaging per 1000 Medicare beneficiaries per state, overall state health, and percentage of the population enrolled in Medicare in each state were used to compare trends with both sets of RRDI.

Results:
West Virginia (WV) had the greatest curiosity for the term “radiologists,” followed by Mississippi (MS) and Arkansas (AR) (RSV: 100, 95, 87, respectively). Oregon demonstrated the lowest level of curiosity for “radiologists,” followed by Vermont and California (RSV: 43, 49, 50, respectively). The highest R densities were found in Washington DC (2.5), Massachusetts (MA) (1.5), and Minnesota (MN) (1.5); the lowest R densities were found in Maine (0.68), MS (0.69), and Nevada (0.7). The RRDI general was greatest in MS (100), WV (85), and AR (82) and smallest in DC (16), MA (25), and MN (28). The highest RM densities were found in Montana (3.3), DC (1.6), and Wyoming (WY) (1.1); the lowest RM densities were found in Oklahoma (0.4), Texas (TX) (0.4), and Utah (0.4). RRDI Medicare was greatest in Louisiana (100), AR (95) and TX (86), and smallest in MN (11), DC (18), and WY (28). Positive trends between medical imaging per 1000 Medicare beneficiaries and state overall health and the RRDI were recognized. No trend was noted between each state’s RRDI and percentage of population enrolled in Medicare.

Conclusion:
Higher RRDI trended with higher imaging utilization, an indirect form of demand, and lower overall health scores per state. The RRDI may be used as a potential tool to determine where R and RM are needed (by predicting demand) in the United States.