Implementasi Anestesi Perioperatif Berbasis Artificial Intelligence (Studi Mixed Methods Exploratory di RS J. H. Awaloei)
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Human resource and time constraints in perioperative anesthesia at J.H. Awaloei Hospital where anesthesiologist-to-patient ratio is 1:12 and a 4 to 6-hour pre-anesthesia window are associated with complications, surgical delays, and costs. This study assessed the impact of artificial intelligence (AI) on operational efficiency, clinical safety, and financial performance using a sequential exploratory mixed-methods design with in-depth interviews from 18 key informants, a staff survey (n=40), and a 20-case time-motion comparison of traditional versus AI-assisted workflows; 2023 hospital audits informed the baseline, and study activities ran from December 2024 to May 2025. Analyses used descriptive statistics and appropriate tests; costing adopted a hospital perspective with 2024 prices, chiefly inpatient accommodation. AI use was associated with an 86.2% reduction in assessment time, operating room utilization rising to 82–84%, a 30% increase in risk detection, 18–22% fewer complications, a 40% reduction in 30-day readmission, and projected annual savings. The results indicate improvements with potential to strengthen predictive risk governance and cost efficiency; broader, risk-adjusted evaluations are warranted to confirm generalizability.
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Author Biographies
Magister Manajemen, Universitas Adhirajasa Reswara Sanjaya, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Magister Manajemen, Universitas Adhirajasa Reswara Sanjaya, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Magister Manajemen, Universitas Adhirajasa Reswara Sanjaya, Kota Bandung, Provinsi Jawa Barat, Indonesia.
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