Artificial Intelligence-Based Internal Quality Assurance Cell (IQAC) Framework for Continuous Quality Improvement and Patient-Centered Care in Multi-Specialty Hospitals

Abstract

Internal Quality Assurance Cells (IQACs), long established in higher education for continuous institutional quality monitoring, are increasingly being adapted by multi-specialty hospitals as a structured mechanism for sustaining accreditation compliance and driving patient-centered care improvement. This paper examines the design and effectiveness of an Artificial Intelligence (AI)-based IQAC framework for continuous quality improvement in multi-specialty hospitals, focusing on predictive quality-indicator monitoring, automated incident reporting, natural-language-processing-based patientfeedback analysis, and real-time compliance dashboards aligned with National Accreditation Board for Hospitals and Healthcare Providers (NABH) and Joint Commission International (JCI) standards. Primary data was collected through a structured questionnaire administered to 120 quality managers, hospital administrators, and clinical audit personnel across multi-specialty hospitals, supplemented by secondary data from published accreditation reports and academic literature. Descriptive statistics, weighted-mean ranking, chi-square tests, and correlation analysis were used to interpret the data. Findings indicate that predictive adverse-event monitoring and automated patient-feedback analysis are the most valued AI-IQAC functions, and that hospitals with AI-augmented IQAC frameworks report measurably faster corrective-action turnaround and higher patient-satisfaction scores than those relying on manual quality-monitoring processes alone. Key barriers identified include staff resistance to algorithmic decision support, data-integration challenges across legacy hospital information systems, and the upfront cost of implementation. The study concludes with recommendations for phased AI-IQAC adoption, staff training, and governance safeguards to support continuous quality improvement and patientcentered care in multi-specialty hospital settings.