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@article{193847,
        author = {Mr. Atul Kumar Mishra and Mr. Peter Muturi Kinuthia and Mr. Arlies Meta Nugraha},
        title = {IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTHCARE QUALITY AND OUTCOMES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1824-1835},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193847},
        abstract = {Background: Artificial intelligence (AI) is rapidly transforming healthcare delivery systems worldwide, promising substantial improvements in diagnostic accuracy, operational efficiency, and patient outcomes. Despite growing enthusiasm, empirical comparative evidence from multi-specialty hospital settings particularly in the Indian healthcare context remains limited. Objective: This study examines the measurable impact of AI-driven clinical tools on healthcare quality indicators and patient outcomes across five major multi-speciality hospitals in India. Methods: A mixed-methods approach combining quantitative analysis and structured interviews was deployed across 385 respondents comprising physicians, nurses, hospital administrators, and patients. Data were collected from January 2024 to December 2025. Statistical analyses included paired t-tests, ANOVA, Pearson correlation, and regression modelling. Results: AI-integrated hospitals demonstrated statistically significant improvements across all key performance indicators. Diagnostic accuracy improved by an average of 17.4 percentage points (p < 0.001). Hospital readmission rates declined from 18.5% to 11.2%, and average length of stay decreased from 6.8 to 4.9 days. Patient satisfaction scores rose from 71.3% to 88.6%. Cost savings of 35–45% were recorded across diagnostic workup and administrative processing categories. Conclusions: AI integration delivers measurable, statistically significant improvements in healthcare quality and outcomes. However, barriers including data privacy concerns (82%), high implementation costs (74%), and algorithmic bias (58%) must be systematically addressed for sustainable adoption.},
        keywords = {Artificial Intelligence, Healthcare Quality, Patient Outcomes, Diagnostic Accuracy, Machine Learning, Clinical Decision Support Systems, India},
        month = {March},
        }