This book starts with discussions over how large language models are used in healthcare and the opportunities we have to change portions of the healthcare paradigm. There are amazing opportunities to save both time as a business unit as well as improve patient experiences.
This book starts with discussions over how large language models are used in healthcare and the opportunities we have to change portions of the healthcare paradigm. There are amazing opportunities to save both time as a business unit as well as improve patient experiences.
Jeremy Harper is President of Owl Health Works, a consulting firm providing quality management, health informatics, and business services for their clients. He has 20 years of healthcare industry experience including academic medical centers, community hospitals, and software vendors. As an executive, his responsibilities have included planning, implementation, and management of deployments and enterprise-enhancing initiatives. He is an authority for best practices in artificial intelligence/machine learning, business strategy, data management, transformations, turnarounds, and organization growth strategies.
Inhaltsangabe
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 Healthcare Chapter 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
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 Healthcare Chapter 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
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