Implementing Health Care AI into Clinical Practice

Overview

Equipping Clinicians to Implement Successful AI Solutions in Health Care 

As artificial intelligence and machine learning emerge as powerful tools to transform patient care, organizations seek professionals skilled in applying these technologies within clinical settings.

The successful implementation of AI solutions in clinical practice requires teams that are skilled in multiple disciplines, including data science, user-centered design, subject-matter expertise, change management, and more. However, the health care field currently lacks professionals who sit at the intersections among those disciplines. This executive education program equips clinicians and executives with the cross-disciplinary knowledge and skills needed to ensure AI solutions are implemented successfully in clinical practice.

Through case studies, small group discussions, and interactive sessions led by Harvard faculty, participants will gain hands-on experience in each step of the AI implementation process. Topics include understanding and defining the problem at hand, as well as tailoring potential solutions to address the problem and meet user needs. Participants will learn how to find relevant data, deploy effective feature engineering, select the appropriate evaluation metrics and model type, design an effective implementation, and develop an iterative mindset for solution building.

This program builds upon AI for Health care: Concepts and Applications. Both courses can be taken independently; however, to earn the Business Applications for AI in Health Care Certificate of Specialization, you must take two of three programs in our health care AI portfolio. Without a strong background in AI, it is recommended that you take AI for Health Care: Concepts and Applications before either Care or Implementing Health Care AI in Clinical Practice or Innovation with AI in Health Care as AI for Health Care: Concepts and Applications covers more foundational concepts upon which the other two programs build. 

Objectives & Highlights

Learning Objectives

Analyze clinical workflows with a focus on the clinical decision that can be improved with AI, and design new AI-enhanced workflows

• Understand the nuances of different metrics to assess the performance of different AI models for specific use cases
• Grasp the concepts of MLOps and machine learning model deployment
• Identify the potential for model drift and how to account for it in a model maintenance plan
• Plan and conduct change management for AI-powered process changes
• Identify what team members—data engineers and scientists, MLOps specialists, and others—you need to fully implement a clinical AI project

Program Highlights

• Hear from experts in the implementation of AI into clinical practice, from health care institutions to health care tech CEOs
• Work through a real-world case study with the guidance of experts in the implementation of AI solutions into clinical practice
• Network with like-minded leaders from across the world through small group discussions in an interactive virtual environment.

Implementing Health Care AI into Clinical Practice will focus on four key pillars: workflow assessment and system engineering; accuracy evaluation and model selection; MLOps:
model deployment and maintenance; and change management.

Upon completion of this course, participants will be able to:

• Analyze a health care problem that may be solved with AI by using design principles
• Describe the different stages of AI development and implementation, and what skills are needed to successfully advance the project
• Identify necessary professional skills required in a successful health care AI team
• Guide a multidisciplinary team through the process of developing and implementing an AI solution into clinical practice
• Design a plan that includes the different elements of an AI health care project: workflow analysis, data and modeling, deployment and change management

Faculty

Current faculty, subject to change.

Santiago Romero-Brufau, MD, PhD

Program Director

August 5 – 8, 2024

Director of AI and Systems Engineering
Department of Otolaryngology — Head and Neck Surgery
Mayo Clinic

Adjunct Assistant Professor
Department of Biostatistics
Harvard T.H. Chan School of Public Health

Agenda

Who Should Participate

This online program is designed for clinicians, administrators, and executives who are responsible for implementing AI solutions in their organization.

Participants will come from a range of organizations including health care delivery (hospitals, clinics, primary care systems, and other health care delivery companies or institutions), health care technology, payers, and governments. Some titles represented in the program will include:

• Clinician
• Physician Leader
• Chief Executive Officer
• Chief Information Officer
• Chief Innovation Officer
• Chief Medical Informatics Officer
• Chief Medical Officer
• Data Scientist
• Director
• Innovation Specialist
• Implementation Specialist
• Product Manager

This program is designed for those who have either a background in data science or a general understanding of how artificial intelligence works; as well as a general understanding of the health care system. It is recommended that those with no artificial intelligence background or knowledge take the first program in the AI Certificate of Specialization series, AI for Health Care: Concepts and Applications, before enrolling in this program.