Quantum Computing–Enabled Clinical Decision Support System using Electronic Health Records and Clinical Documentation

Abstract

The rapid growth of Electronic Health Records (EHRs), along with the need for accurate, real-time clinical documentation, has pushed traditional computing methods to their operational limits. Conventional machine learning techniques often fall short when handling the heterogeneous, high-dimensional nature of healthcare data—especially in situations where clinical decisions must be made quickly and reliably. To overcome these challenges, this study introduces a Quantum Computing–Enabled Clinical Decision Support System (QC-CDSS), which integrates quantum machine learning (QML) with advanced deep learning models to improve diagnostic accuracy and predictive performance. The proposed framework utilizes quantum-driven feature extraction, hybrid variational quantum circuits, and quantum kernel-based classifiers to process both structured EHR records and unstructured clinical narratives. In parallel, sophisticated natural language processing methods, including transformer-based architectures, are employed to capture semantic, contextual, and temporal patterns from physician notes, discharge summaries, radiology interpretations, and related clinical text. The convergence of these techniques—particularly the integration of QML—presents promising opportunities for achieving more effective decision support and faster analysis of high-dimensional medical data. This paper discusses the core concepts, major applications, existing challenges, and future research pathways for incorporating quantum-enhanced machine learning into clinical decision-making systems, emphasizing their potential to address complex healthcare problems more efficiently.