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Original research
INTEGRATING OPERATIONS RESEARCH, ANALYTICS, AND QUALITY MANAGEMENT FOR HEALTH SYSTEMS TRANSFORMATION: LESSONS FROM MANUFACTURING AND HOSPITAL SERVICE PRACTICEPages 469-478
Abstract:
Modern hospital care systems face severe operational friction, rising costs, dynamic demand, and workflow fragmentation. To solve these challenges, healthcare management must synthesize foundational quality management methodologies—derived from industrial paradigms—with advanced operations research (OR), machine learning analytics, and predictive modeling. This study examines the convergence of Total Quality Management (TQM), Lean Six Sigma (DMAIC), Toyota Production System principles, queueing theory, mathematical optimization, and predictive algorithms across clinical environments, MedTech platforms, and health system operations. By integrating theoretical frameworks (Deming, Juran, Crosby) with empirical data from cardiac cath lab capacity planning, bed throughput optimization, outpatient-to-inpatient conversion modeling, and MedTech funnel refactoring, this paper articulates a unified model for health governance and operational transformation. Empirical results demonstrate that cross-functional OR-analytics interventions reduce patient wait times by 40%, increase bed turnover by 50%, double MedTech conversion rates, and generate substantial margin enhancements without requiring capital expansion.
Keywords:
Total Quality Management, Lean Six Sigma, Queuing, Manufacturing, Hospital, Wellness.
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Balaji Gopalan, 
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