Are your clinical operations facing delays due to multiple vendors, fragmented systems, or increasing trial complexity? At MaxisIT, we specialize in empowering clinical teams with cutting-edge AI-enabled solutions that streamlines clinical trial management, optimizes your data accuracy, and empowers smarter decision-making, tailored to your individual trial design.
Description: Geriatric patients with non-small cell lung cancer (NSCLC) and late-stage diabetes mellitus complications represent a highly complex and vulnerable population. Co-morbidities, co-medications, age-specific frailty, and the interplay between cancer progression and diabetic complications make optimizing treatment outcomes very challenging. Chemotherapy exacerbates these complications, increasing risks of adverse events, and impacting quality of life. Causal graphs, rooted in graph theory and causal inference principles, provide a framework to address these complexities by uncovering causal relationships in real-world data (RWD), enabling personalized treatment strategies. This framework allowed MaxisIT to disentangle the intricate interplay between NSCLC, diabetes complications, and chemotherapy outcomes, accounting for confounding factors like age, comorbidities, and baseline health status, to personalize treatment for better patient outcomes and clinical decision-making.
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