The system achieved a 96.43% sensitivity rate, proving its effectiveness in detecting eligible patients.
Innovative Patient Identification
Artificial Intelligence (AI)-based patient identification support system to streamline the identification of eligible CAR-T candidates.
Multicenter Collaboration
Three spanish centers participated, demonstrating the system’s adaptability across different hospital settings.
Promising Results
The AI algorithm screened over 4 million patient records, identifying 34 potential candidates, with a 79.4% accuracy confirmed by hematologists.
context
Chimeric Antigen Receptor T-cell (CAR-T) therapy has revolutionized the treatment of certain hematologic cancers, such as lymphomas and leukemias, providing new hope for patients with limited options. However, early identification of suitable candidates and efficient management until CAR-T infusion remain significant challenges.
This pilot study, conducted by the Spanish Hematopoietic Transplant and Cellular Therapy Group (GETH-TC) in collaboration with IOMED, introduces an artificial intelligence (AI)-driven system using real-world data (RWD) to improve patient identification for CAR-T therapy, reducing missed candidates and accelerating treatment timelines.
the challenge
CAR-T therapy is a life-changing option for patients with refractory hematologic malignancies. However, identifying candidates in a timely manner and ensuring a smooth process until infusion is critical for treatment success. Traditional selection methods can be slow and error-prone, highlighting the need for innovative solutions to optimize this process.
The primary goal was to reduce missed CAR-T candidates and accelerate infusion timing through an artificial intelligence (AI)-based patient identification support system.
how iomed helped
IOMED implemented its AI-powered Data Space Platform that extracts clinical concepts from free-text medical records and EHRs, harmonizing data according to the OMOP-CDM model. This plataform allowed participating hospitals to rapidly identify patients meeting predefined criteria for CAR-T therapy, improving efficiency and accuracy in patient selection.
Results
Patients were considered accurately identified
79,4%
Sensitivity: Only one patient meeting the criteria was missed due to variations in abbreviations used for diffuse large B-cell lymphoma.
96,4%
Patients identified as potential CAR-T candidates
34
impact 1
Hospital
Optimized resource use and improved efficiency in patient identification, enabling faster and more precise care.
impact 2
Industry
Better identification of potential candidates for clinical trials and treatments, facilitating the development and implementation of advanced therapies.
impact 3
Patients
Faster access to potentially life-saving treatments and reduced risk of being overlooked for CAR-T therapy.
impact 4
Community
Advancing the integration of innovative technologies in healthcare, promoting more personalized and effective patient care.
Testimonials
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Gerardo Ontiveros
Coordinador IT de la Unidad de Salud Digital
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Gerardo Ontiveros
Coordinador IT de la Unidad de Salud Digital
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