AI and Cloud Computing: Streamlining Healthcare Operations (Published)
Artificial Intelligence and cloud computing technologies are fundamentally transforming healthcare operations by creating unprecedented opportunities for operational efficiency and enhanced patient care delivery. The convergence of these technologies represents a paradigm shift in healthcare management, moving beyond traditional constraints of on-premises systems to enable scalable, flexible infrastructure that supports complex computational demands. Cloud computing provides the essential backbone for deploying sophisticated AI applications, facilitating real-time data processing, predictive analytics, and automated decision support systems. This technological synergy addresses persistent healthcare challenges through intelligent automation of administrative tasks, advanced medical record management, and evidence-based clinical decision support. The implementation of AI-powered systems significantly reduces administrative burdens on healthcare professionals, allowing increased focus on direct patient care while improving diagnostic accuracy and treatment outcomes. Healthcare organizations benefit from optimized resource utilization, reduced medical errors, and enhanced revenue cycle management. However, successful implementation requires careful navigation of substantial challenges, including cybersecurity vulnerabilities, regulatory compliance complexities, and algorithmic bias concerns. The transformative potential of these technologies extends beyond individual institutions to enable global healthcare collaboration and population health management initiatives, ultimately promising more efficient, equitable, and patient-centric healthcare delivery systems.
Keywords: Artificial Intelligence, Clinical Decision Support, Cloud Computing, Digital Transformation, healthcare operations
Real-Time Healthcare Workforce Rescheduling using a Quantum Computer: A Novel Approach to Dynamic Staff Allocation in Hospital Settings (Published)
Real-time healthcare workforce scheduling represents a critical optimization challenge with direct implications for patient care quality and operational efficiency. This article introduces a quantum computing approach to dynamic hospital staff rescheduling that addresses the complex constraints of medical workforce allocation when facing unexpected absences. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum processing, demonstrate how quantum annealing and gate-based quantum algorithms can rapidly identify optimal staff substitutions while balancing factors such as specialist qualifications, shift duration, fatigue management, and geographical proximity. The simulation results indicate that quantum-accelerated scheduling can significantly reduce the average time to fill critical absences compared to classical heuristic methods while maintaining high adherence to staffing quality metrics. This has substantial potential to mitigate the downstream clinical impacts of staffing disruptions, particularly in high-acuity hospital departments where timely specialist presence is essential for patient outcomes.
Keywords: QUBO optimization, healthcare operations, healthcare workforce management, quantum computing, staff scheduling