In today’s rapidly evolving healthcare environment, one technology stands at the forefront of innovation: large language models (LLMs). Far more than a fleeting hype, LLMs represent a foundational shift in how healthcare professionals interact with and derive value from data. From simplifying clinical note-writing to supporting patient engagement and enhancing administrative processes, LLMs have the power to transform nearly every corner of the healthcare ecosystem. In Large Language Models (LLMs) for Healthcare, Jeremy Harper shines a spotlight on this transformative potential. With clarity and practicality, he explores how these advanced artificial intelligence (AI) tools can reshape clinical workflows, optimize administrative tasks, and ultimately create a more responsive, patient-centered model of care. Over the course of this book, you will discover new opportunities—learn how LLMs can reduce manual documentation burdens, provide intelligent summaries of complex patient histories, and offer real-time translations of clinical jargon; understand the fundamentals—grasp what LLMs are, how they work, and why they can handle vast amounts of clinical text more effectively than previous AI tools; examine key use cases—from automated billing support and smart note generation to patient triage and ethical telehealth consultations; address risks and realities—gain insight into challenges such as "hallucinations," inherent bias, and the critical importance of patient privacy; plan for implementation—explore strategies for prompt engineering, fine-tuning, and rigorous evaluation of LLM solutions; and envision the future – glimpse how LLMs might revolutionize healthcare through enhanced back-office operations and cutting-edge clinical decision support.

Table of Contents:
Chapter 1: Introduction to Large Language Models for HealthcareChapter 2: Who Makes the BEST ExpertChapter 3: Understanding the Technology Behind LLMsChapter 4: The Current State of LLMs in HealthcareChapter 5: The Data that Feeds LLMsChapter 6: Basic Prompt EngineeringChapter 7: Prompt Engineering versus Fine-TuningChapter 8: In-house Development of LLMs for Healthcare ApplicationsChapter 9: Evaluating LLM Vendors' Maturity for HealthcareChapter 10: Bias in LLMs and Its Implications for HealthcareChapter 11: Ensuring Compliance and Ethical UseChapter 12: LLMs in Clinical Decision Support SystemsChapter 13: Patient Engagement and LLMsChapter 14: Training and Educating Healthcare Professionals on LLMsChapter 15: Security and Privacy Concerns with Healthcare LLMsChapter 16: The Role of Interdisciplinary Teams in LLM ProjectsChapter 17: Implementing LLM SolutionsChapter 18: Integration with Electronic Health RecordsChapter 19: Measuring the Impact of LLMs in HealthcareChapter 20: Looking Ahead: The Future of Healthcare with LLMs References