Health Care Transformation

Program Overview

The 18-week Health Care Transformation program is designed to offer an integrated experience of two online executive programs from Harvard Medical School —AI in Health Care and Leading Digital Transformation in Health Care— along with additional learning modules to deliver a highly impactful curriculum on digital transformation and AI in health care. Participants will gain the expertise to implement forward-looking digital strategies, establish robust AI adoption frameworks, and leverage the potential of scaling AI technologies for innovation.

Curriculum

This learning journey offers an all-encompassing, in-depth exploration of digital transformation and AI in health care, enabling professionals to acquire new knowledge and develop advanced skills. Participants will complete two executive programs—AI in Health Care and the Leading Digital Transformation in Health Care—with additional learning modules.

As a participant you will:

Develop a comprehensive framework for implementing AI at scale within health care organizations to enhance operational efficiency and patient care.
Strategize and manage the adoption of emerging digital technologies effectively to drive innovation and streamline health care processes.
Demonstrate a thorough understanding of the challenges involved in scaling AI solutions, and devise strategies to overcome these obstacles in health care settings.
Create detailed tactical and strategic plans for integrating and implementing advanced technologies, ensuring their transformative impact on health care delivery.
Identify new opportunities for AI in health care to address unmet needs.
Assess the ethical implications and potential biases of AI technologies in health care settings.
Recognize the lessons learned from real-world digital health transformation experiences and pilots, and create your own action plan through the capstone project.
Identify the elements of effective change management as well as the critical success factors for implementing digital transformation within your organization.
Apply best practices and program insights to day-to-day operations and long-term strategic priorities.

AI in Health Care: From Strategies to Implementation

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Leading Digital Transformation in Health Care

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Part 1 — AI in Health Care: From Strategies to Implementation

Module 1: The History and Foundations of AI
Module 2: A Framework for the AI Development Pipeline
Module 3: From the Lab to the Real World
Module 4: Transparency, Reproducibility, and Generalizability in AI
Module 5: The Potential for Bias and Harm in AI
Module 6: AI for Startups
Module 7: AI for Wearable Data
Module 8: Live Session — Capstone Presentations

Part 2 — Leading Digital Transformation in Health Care

Module 1: Transforming, Disrupting, or Staying Competitive
Module 2: Enabling Technologies
Module 3: Transformation Management Skills and Practices — Fostering Change Management
Module 4: Transformation Management Skills and Practices — Establishing Digital Transformation and Innovation
Module 5: Transformation Management Skills and Practices — Creating a Digital Culture
Module 6: Creating the Transformation Plan

Part 3 — Exclusive Learning Modules

Module 0: Program Orientation
Explore the program's objectives and expected learning outcomes, and gain an overview of the health care industry, focusing on the transformative role of AI and digital technologies.
Module 1: Management Education
Delve into the fundamental concepts of management education in health care, and discuss various leadership styles and effective management practices.
Module 2: Organizational Learning
Discover strategies for fostering a learning organization within the health care sector, emphasizing the importance of continuous professional development, and describing the potential business value for AI pilots.
Module 3: Innovation Management
Examine the process of innovation in health care, and gain tools and techniques for managing innovation effectively by proposing an approach for integrating an AI innovation group.
Module 4: Framework Analysis 1
Analyze techniques for evaluating AI implementation frameworks and their applicability in health care. Analyze the elements of a peer's framework for implementing AI, and integrate peer feedback.
Module 5: Dealing with Vendors
Delve into best practices for selecting and managing AI vendors, including critical considerations for vendor evaluation and relationship management. Describe diligence activities associated with examining an AI-based product or service.
Module 6: Managing AI Limitations and Potential Problems
Identify and manage potential challenges in AI implementation, and develop strategies for mitigating risks and addressing limitations.
Module 7: Framework Analysis 2
Refine AI implementation frameworks, incorporating feedback to enhance framework effectiveness. Analyze the elements of a peer's framework for implementing AI, and integrate peer feedback.
Module 8: Mid-Program Summary
Discuss the application of AI insights in real-world health care settings, and reflect on the learnings gained in approaching AI solutions in health care.
Module 9: Strategic Context
Set the strategic context for AI in health care by aligning AI initiatives with organizational strategy. Defend the AI strategic context selected for an organization.
Module 10: Governance
Explore governance frameworks for AI implementation, and outline a charter for an AI governance committee.
Module 11: Talent Management
Examine the potential impact of AI on organizational talent management in health care, and describe the potential impact of AI on an organization's talent needs.
Module 12: Data Management
Delve into best practices for managing data in AI projects by analyzing data management practices to ensure data quality, security, and compliance.
Module 13: Technology Architecture
Assess the impact of AI on technology architecture, and describe methods used to assess the impact of an AI-based application on an organization’s infrastructure.
Module 14: Framework Analysis 3
Discover advanced techniques for framework analysis, enhancing the effectiveness of AI implementation frameworks. Analyze the elements of a peer's framework for implementing AI, and integrate that feedback into your framework.
Module 15: Implementation Issues
Compare successful and unsuccessful AI implementation projects, and learn from failures and best practices.
Module 16: Industry Participation
Explore and discuss the importance of participating in AI-centered industry forums and communities.
Module 17: Framework Analysis 4
Finalize and refine AI implementation frameworks, preparing them for real-world application. Analyze the elements of a peer's framework for implementing AI, and integrate that feedback into your framework.
Module 18: Program Wrap-Up
Summarize program learnings, and engage in final discussions while exploring future directions in AI for health care. Develop a comprehensive framework for implementing AI.