Journal of cardiopulmonary rehabilitation and prevention · 2026
An Algorithm for the Optimal Location of Cardiac Rehabilitation Centers: A SPATIAL PERSPECTIVE.
СтатьяНазвание и аннотация: Перевод в очереди
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de Lima Moraes Thiago Mauro, da Silva Luís Felipe Leite, Dos Santos Mauro Augusto, Dos Reis Jaccoud Bruno, Gregorio Ronaldo Malheiros, de Souza Filho Erito M
Аннотация
PURPOSE: Geographical distance from a patient's residence to a cardiac rehabilitation (CR) clinic is a significant barrier to the initiation and continuation of CR programs worldwide. This distance can adversely affect patient adherence and clinical outcomes, particularly among patients with high cardiovascular risk who require frequent and consistent CR sessions. Addressing this barrier may enhance the accessibility and effectiveness of CR services.
METHODS: A p -median optimization model was developed to strategically allocate resources for CR in Rio de Janeiro, Brazil. Six scenarios were created to accommodate patients with high cardiovascular risk, with the aim of identifying optimal clinic locations to minimize the average travel distance for patients.
RESULTS: The implementation of 4 additional CR clinics demonstrated a 14% to 17% improvement in accessibility, as indicated by a reduction in travel distances for the target patient population. The model also revealed a concentration of clinics within a single region of the city, underscoring disparities in access and highlighting the inequitable distribution of CR services.
CONCLUSIONS: The optimal allocation of CR clinics using an optimization model can significantly enhance healthcare system efficiency. This approach may ensure optimal resource distribution and treatment accessibility, aligning with the principles of value-based healthcare.
METHODS: A p -median optimization model was developed to strategically allocate resources for CR in Rio de Janeiro, Brazil. Six scenarios were created to accommodate patients with high cardiovascular risk, with the aim of identifying optimal clinic locations to minimize the average travel distance for patients.
RESULTS: The implementation of 4 additional CR clinics demonstrated a 14% to 17% improvement in accessibility, as indicated by a reduction in travel distances for the target patient population. The model also revealed a concentration of clinics within a single region of the city, underscoring disparities in access and highlighting the inequitable distribution of CR services.
CONCLUSIONS: The optimal allocation of CR clinics using an optimization model can significantly enhance healthcare system efficiency. This approach may ensure optimal resource distribution and treatment accessibility, aligning with the principles of value-based healthcare.