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"Clinical Procedures for Medical Assistants provides clear, step-by-step instructions for common office procedures such as taking vital signs, collecting and processing lab specimens, preparing patients for examinations, and assisting with office surgeries. Written by expert educator Kathy Bonewit-West, this edition covers the latest competencies and topics related to today's medical assisting practice, including HIPAA, the Electronic Health Record, and caring for special populations."

"Clinical Procedures for Medical Assistants provides clear, step-by-step instructions for common office procedures such as taking vital signs, collecting and processing lab specimens, preparing patients for examinations, and assisting with office surgeries. Written by expert educator Kathy Bonewit-West, this edition covers the latest competencies and topics related to today's medical assisting practice, including HIPAA, the Electronic Health Record, and caring for special populations."

"Clinical Procedures for Medical Assistants provides clear, step-by-step instructions for common office procedures such as taking vital signs, collecting and processing lab specimens, preparing patients for examinations, and assisting with office surgeries. Written by expert educator Kathy Bonewit-West, this edition covers the latest competencies and topics related to today's medical assisting practice, including HIPAA, the Electronic Health Record, and caring for special populations."

"Clinical Procedures for Medical Assistants provides clear, step-by-step instructions for common office procedures such as taking vital signs, collecting and processing lab specimens, preparing patients for examinations, and assisting with office surgeries. Written by expert educator Kathy Bonewit-West, this edition covers the latest competencies and topics related to today's medical assisting practice, including HIPAA, the Electronic Health Record, and caring for special populations."

"Perkembangan teknologi kecerdasan buatan (AI) dalam beberapa tahun terakhir telah menjadi peluang besar di berbagai sektor, termasuk dalam sektor kesehatan. Salah satu implementasi AI yang menunjukkan potensi signifikan adalah Large Language Models (LLM), yang dapat memahami dan menghasilkan teks. Penelitian ini bertujuan untuk mengembangkan sistem asisten medis virtual berbasis AI dengan mengintegrasikan LLM dan teknik Retrieval-Augmented Generation (RAG). Sistem ini dirancang untuk memberikan informasi medis yang relevan, akurat, dan terkini bagi tenaga medis. Metode penelitian melibatkan pengembangan sistem menggunakan LLM, RAG, dan LangChain, yang kemudian diuji untuk memastikan kinerja dan keandalannya. Evaluasi dilakukan dengan metrik berbasis RAG dan ROUGE, mencakup dimensi seperti faithfulness, context precision, answer relevance, dan context recall. Hasil penelitian menunjukkan bahwa integrasi LLM dan RAG mampu meningkatkan akurasi informasi, relevansi jawaban, dan efisiensi sistem dalam skenario klinis. Sistem ini diharapkan dapat menjadi solusi inovatif untuk mendukung pekerjaan tenaga medis, mempercepat pengambilan keputusan, dan meningkatkan kualitas pelayanan kesehatan.

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The advancement of artificial intelligence (AI) technology in recent years has created significant opportunities across various sectors, including healthcare. One notable implementation of AI with significant potential is Large Language Models (LLM), which can comprehend and generate text. This study aims to develop an AI-based virtual medical assistant system by integrating LLM and Retrieval-Augmented Generation (RAG) techniques. The system is designed to provide relevant, accurate, and up-to-date medical information for healthcare professionals. The research methodology involves the development of the system using LLM, RAG, and LangChain, followed by performance and reliability testing. Evaluation is conducted using RAG- and ROUGE-based metrics, covering dimensions such as faithfulness, context precision, answer relevance, and context recall. The results demonstrate that integrating LLM and RAG enhances information accuracy, answer relevance, and system efficiency in clinical scenarios. This system is expected to be an innovative solution to support healthcare professionals, expedite decision-making, and improve the quality of healthcare services. "