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Overview


Are you ready to revolutionize your approach to health care delivery, research, and population health through data science and AI? "Leveraging AI for High-Impact Health Research," a new online course in partnership with Washington University in St. Louis, is designed to help professionals transform their approach to health care delivery, research, and population health through data science and AI. Through real-world scenarios, toolkits, and live sessions with leading AI and academic experts, participants will master AI literacy and practical skills to design, implement and evaluate AI interventions in health research.

Learning Objectives


This course will teach you how to:

  • Integrate advanced AI models into the data science lifecycle and utilize data science tools effectively.
  • Identify and understand the best data sources for various AI applications.
  • Select and apply the most effective strategies for implementing LLM models in health research practices. 
  • Discover the processes behind AI funding mechanisms and learn how to secure funding for AI-related projects.

Competencies by Module


  • GPT Configuration and Prompt Engineering: 
    • Understanding large language models (LLMs) capabilities and potential applications 
    • Understanding GPT architecture and skills in configuring GPT models for specific biomedical informatic tasks. 
  • Data Transformation and Integration: 
    • Competence in handling various data formats, including numeric, categorical, and text-based. 
    • Skills in transforming and integrating heterogeneous data sources for machine learning applications to ensure data compatibility and efficiency. 
  • Machine Learning Configurations and Model Selection: 
    • Knowledge of machine learning configuration, including selecting and pre-processing algorithms. 
    • Skills in identifying the strengths and weaknesses of different algorithms for specific use cases. 
  • Algorithm Performance Comparison and Optimization: 
    • Competence in comparing and optimizing the performance of various algorithms and identifying the most effective models for specific tasks. 
    • Evaluate the performance of AI tools, such as ambient AI, and LLMs, to ensure they meet project goals, ethical, and quality standards. 

Module Descriptions


Module 1: Foundations of AI in Health Research
Explore the history, evolution, and impact of AI in health care–particularly in medical imaging and electronic health records. Learn key concepts to guide effective selection and use of AI tools–and to understand how these integrate into the data science lifecycle.

Module 2: Data Use and Engineering
Learn how to extract valuable insights from diverse health care data sources, and how to identify those data sources best suited for various AI applications. This module covers the hierarchy of data types, data manipulation techniques, and the challenges of working with both structured and unstructured data to improve AI models.

Module 3: Best Practices in AI Implementation
Gain practical knowledge on selecting and applying strategies for implementing AI in health research. Learn best practices for AI development, evaluation, and governance, with a focus on prompt engineering and real-world applications. Understand the emerging standards and challenges in AI reporting.

Module 4: AI Funding Mechanisms
Navigate the complexities of securing funding for AI projects in health care, by learning about different funding sources or mechanisms– as well as maturity models to assess project readiness, and key ethical considerations. Gain insights on how to secure funding for your AI-related projects by aligning your initiatives with funder priorities, processes, and requirements.

Course Outline


  • Registration Closes: September 20, 2024
  • September 17: Enrollees Receive Orientation Package with Short Pre-Work Assignment
  • September 23: Enrollees Receive Log In Information to Access the Learning Platform 
  • September 30, 10:30a.m.-12:00p.m. ET: Live Interaction Call with Facilitation
  • October 7, 11:00a.m.-12:30p.m. ET: Live Interaction Call with Facilitation
  • October 14, 10:30a.m.-12:00p.m. ET: Live Interaction Call with Facilitation
  • October 21, 10:30a.m.-12:00p.m. ET: Live Interaction Call with Facilitation

Registration Rates


MemberNon-Member
$999$1350

Additional Information


Receipts & Confirmations

Detailed receipts can be found in “My Orders” via your AcademyHealth account, or available upon request. If you would like one, please send a request to registrations@academyhealth.org. Payment receipts will be sent to the email address provided on the registration form.

Cancellations
All cancellations must be received in writing or via email by Monday, August 26, 2024. Registration fees for cancelled registrants may not be applied to future AcademyHealth meetings.

Faculty


Presenter

Adam Wilcox, Ph.D.

Associate Lead - Washington University School of Medicine in St. Louis

Dr. Wilcox is a Professor of Medicine and the Director of the Center for Applied Clinical Informatics, Institute for Informatics, Washington University in St. Louis School of Medicine. Read Bio

Presenter

Aditi Gupta, Ph.D.

Assistant Professor - Division of Biostatistics Washington University School of Medicine

Dr. Aditi Gupta, is an Assistant Professor in the Washington University Institute for Informatics and the Division of Biostatistics. Read Bio

dr_payne
Presenter

Philip Payne, Ph.D., FACMI, FAMIA, FAIMBE, FIAHSI

Director, Institute for Informatics, Data Science and Biostatistics - Washington University School of Medicine in St. Louis

Dr. Payne is an internationally recognized leader in the field of clinical research informatics (CRI) and translational bioinformatics (TBI). His research portfolio is actively supported by a combination of NCATS, NLM, and NCI grants and contracts, as well as a variety of awards from both nonprofit and philanthropic organizations. Read Bio

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Presenter

Tara Borlawsky-Payne, M.A., FAMIA

Instructor - Washington University School of Medicine in St. Louis

Ms. Borlawsky-Payne is a lecturer in the WashU Institute for Informatics, Data Science & Biostatistics. Read Bio