Optimalisasi Rekam Medis Elektronik Berbasis Artificial Intelligence - Rinaldi, Ikhwan; Sulistiadi, Wahyu (UI Scholars Hub, 2025)
Electronic medical records (EMRs) are essential to modern healthcare systems, yet their implementation continues to face challenges such as disruption of doctor–patient communication, administrative burden, and documentation structures misaligned with clinicians’ clinical reasoning processes. This narrative review aims to assess the limitations of conventional EMRs and examine the potential of artificial intelligence (AI) to improve documentation efficiency, accuracy, and overall clinical performance. The literature indicates that AI technologies, including machine learning, deep learning, and natural language processing, can automate documentation, streamline workflows, enhance decision support, and reduce physician burnout, thereby allowing clinicians to devote more attention to patient care. Nevertheless, the integration of AI into EMRs must address ethical concerns such as algorithmic bias, data privacy, and accountability for AI-assisted decisions. In conclusion, AI-driven EMR optimization has the potential to create a more human-centered, efficient, and data-driven documentation ecosystem when supported by strong regulatory and ethical frameworks.