TY - JOUR
T1 - Geographical distribution and accessibility to cardiac CT readers in the United States
T2 - A snapshot from the 2022 medicare analysis
AU - El Yaman, Ahmad
AU - El Ghazawi, Alaaeddine
AU - Sayed, Ahmed
AU - Alwan, Maria
AU - Shaikh, Asim
AU - Al Rifai, Mahmoud
AU - Shaw, Leslee J.
AU - Ferencik, Maros
AU - Al-Mallah, Mouaz H.
N1 - Publisher Copyright:
© 2025 Society of Cardiovascular Computed Tomography
PY - 2025/7/1
Y1 - 2025/7/1
N2 - Background: Cardiac computed tomography (CCT) is an increasingly important modality for the diagnosis and management of cardiovascular diseases. However, disparities in the availability of trained CCT readers across the United States limit equal access. Objective: This study examined the geographical distribution and characteristics of CCT readers who billed Medicare for CCT in 2022. Methods: Data from the 2022 Medicare Part B and Medicare Doctors and Clinicians datasets were analyzed to determine the number, specialties, gender, year of graduation, and geographical locations of CCT readers, and the volume of scans. Results: A total of 242,538 scans were billed in 2022, of which 194,895 (80.4 %) were performed by 3,179 CCT readers interpreting 11 or more studies. Sixty-eight percent of readers were radiologists and 32 % were cardiologists. Significant geographic disparities were observed in reader density, with some regions such as Puerto Rico having as few as 1.31 readers per million Medicare beneficiaries, while the District of Columbia had the highest density (147.99 per million). Female representation among CCT readers remained low, with women accounting for 14 % of readers. In addition, approximately 15.7 million US residents were located more than 50 miles away from the nearest CCT reader. Conclusion: The findings highlight significant geographical disparities in access to qualified CCT readers, with millions of citizens living more than 50 miles from the nearest reader. Additionally, there is a marked imbalance in female representation among CCT readers.
AB - Background: Cardiac computed tomography (CCT) is an increasingly important modality for the diagnosis and management of cardiovascular diseases. However, disparities in the availability of trained CCT readers across the United States limit equal access. Objective: This study examined the geographical distribution and characteristics of CCT readers who billed Medicare for CCT in 2022. Methods: Data from the 2022 Medicare Part B and Medicare Doctors and Clinicians datasets were analyzed to determine the number, specialties, gender, year of graduation, and geographical locations of CCT readers, and the volume of scans. Results: A total of 242,538 scans were billed in 2022, of which 194,895 (80.4 %) were performed by 3,179 CCT readers interpreting 11 or more studies. Sixty-eight percent of readers were radiologists and 32 % were cardiologists. Significant geographic disparities were observed in reader density, with some regions such as Puerto Rico having as few as 1.31 readers per million Medicare beneficiaries, while the District of Columbia had the highest density (147.99 per million). Female representation among CCT readers remained low, with women accounting for 14 % of readers. In addition, approximately 15.7 million US residents were located more than 50 miles away from the nearest CCT reader. Conclusion: The findings highlight significant geographical disparities in access to qualified CCT readers, with millions of citizens living more than 50 miles from the nearest reader. Additionally, there is a marked imbalance in female representation among CCT readers.
KW - Cardiac CT
KW - Cardiac CT readers
KW - Geographical mapping
KW - Medicare
UR - https://www.scopus.com/pages/publications/105009513133
UR - https://www.scopus.com/pages/publications/105009513133#tab=citedBy
U2 - 10.1016/j.jcct.2025.06.011
DO - 10.1016/j.jcct.2025.06.011
M3 - Article
C2 - 40592667
AN - SCOPUS:105009513133
SN - 1934-5925
VL - 19
SP - 466
EP - 473
JO - Journal of Cardiovascular Computed Tomography
JF - Journal of Cardiovascular Computed Tomography
IS - 4
ER -