Implementasi Anestesi Perioperatif Berbasis Artificial Intelligence (Studi Mixed Methods Exploratory di RS J. H. Awaloei) - Koleangan, Peter Joshua Alexander; Hidayat, Dasrun; Agung, Wiseto Prasetyo (Jurnal Pengabdian Nasional (JPN) Indonesia, 2025)
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.