Summary

High-performance computing (HPC) stands at a critical inflection point in healthcare, transitioning from a specialized computational tool to a transformative force reshaping care delivery, clinical outcomes, and business models. This thesis provides one of the first systematic frameworks for analyzing this evolution across technological, economic, and business dimensions through an innovative multi-methodological approach combining automated literature analysis, technological case studies, economic modeling, and business model evaluation.
Our automated literature analysis reveals a significant transition in how HPC is conceptualized and deployed in healthcare - evolving from a specialized computational tool into a foundational technology platform that enables system-wide innovation. A key finding highlights how HPC increasingly supports both AI and simulation applications in healthcare, with emerging trends suggesting convergence of these approaches. This capability is explored through our complementary case studies: HPC-enabled computer vision models using lower resolution pathology images demonstrate improved accuracy and efficiency in cancer detection, while CT-based Finite Element Modeling simulations prove cost-effective for osteoporosis screening, particularly for postmenopausal women aged 70 and above. This synergy between simulation and AI capabilities, powered by HPC, is driving broader organizational changes. Healthcare organizations are shifting towards data-centric and patient-focused approaches, increasingly adopting recurring revenue models and recognizing simulation and AI-derived insights as key assets.
Our research provides a generalizable analytical framework for assessing emerging technologies' impact across industries. Our findings suggest that while HPC offers significant potential for improving healthcare quality and efficiency, successful implementation requires careful consideration of technological capabilities, economic factors, and business model dynamics.