Implementing Health Care AI into Clinical Practice
This program is not currently available for registration. Click subscribe to be notified when information becomes available
Summary
Equipping Stakeholders to Implement Successful AI Solutions in Health Care
Medicine relies on processing patient information, and AI can enhance this process to improve patient care. However, integrating AI into clinical practice is complex. The course “Implementing AI in Clinical Practice,” led by Harvard faculty, equips clinicians with hands-on skills through case studies and interactive sessions. Participants will learn strategies for change management, workflow assessment, model selection and evaluation, and Machine Learning Operations (MLOps) to successfully implement AI in healthcare settings.
Program Overview
As Artificial Intelligence and machine learning reshape patient care, organizations need professionals who can apply these technologies in clinical settings. Implementing Health Care AI in Clinical Practice equips those professionals with proven methods and up-to-date insights driven by emerging trends and evolving industry demands.
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. It also remains aligned with
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.
Implementing Health Care AI into Clinical Practice contributes to the AI in Health Care Certificate of Specialization.
This Certificate of Specialization is obtained by taking two of three programs in our health care AI portfolio. Without a strong background in AI, it is recommended that you take Responsible AI for Health Care: Concepts and Applications before either this program or Innovation with AI in Health Care. However, the programs can be taken in any order. For any questions about the programs or Certificate of Specialization, please email enrollment@hsph.harvard.edu
Objectives & Highlights
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, MIOps specialists, and others— you need to fully implement a clinical AI project
- 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
Certificate of Specialization
Advance your Career with a Certificate of Specialization
This program contributes to the 2-program AI in Health Care Certificate of Specialization. Click here for more information.
