AI & Digital Transformation in Clinical Practice

AI & Digital Transformation in Clinical Practice

Instructors: Ashish Mohan

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Understand how AI enhances imaging, decision support, and early disease detection.
Explore how digital tools streamline processes, reduce administrative burden, and improve efficiency.
Learn how AI enables predictive analytics and proactive care strategies.
Gain insight into data readiness, clinician adoption, workflow redesign, ethics, and regulatory awareness.

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There are 4 modules in this course

Artificial intelligence is transforming healthcare, but successful adoption requires more than understanding the technology. It demands the ability to evaluate AI tools, integrate them into clinical workflows, and use them responsibly to improve patient care. This course equips healthcare professionals with practical, non-technical knowledge to apply AI across diagnostics, patient monitoring, clinical operations, and digital transformation initiatives. Learners will explore how AI supports, rather than replaces, clinical judgment while improving efficiency, reducing administrative burden, and enabling data-driven decision-making.

What makes this course unique is its strong focus on real-world clinical application instead of AI development or programming. Through case studies, workflow redesign exercises, hands-on activities, role plays, and a capstone project, learners will develop the confidence to assess AI use cases, manage organizational change, and address ethical, regulatory, and governance considerations. By the end of the course, learners will be prepared to identify high-impact AI opportunities, lead responsible digital transformation initiatives, and implement AI solutions that enhance patient outcomes while maintaining safety, transparency, and clinician accountability.

This module introduces common clinical and operational challenges in the healthcare industry and discusses how AI and digital technologies can help overcome these challenges. By taking a problem-solving approach, learners can analyze problems like diagnostic delays, administrative burden, and workflow inefficiencies, and understand where AI can help make a difference. The module also explains the use of AI as a clinical assistive technology, managing learners' expectations about what AI can and cannot do. By applying AI concepts to real-world clinical problems, learners establish a practical foundation for understanding the role of AI in today's healthcare delivery environment.

What's included

11 videos2 readings1 assignment1 peer review2 discussion prompts

11 videosTotal 80 minutes

Course Welcome and Goals6 minutes
Module Introduction5 minutes
Diagnostic Delays and Decision Fatigue9 minutes
Administrative Overload in Healthcare8 minutes
Care Coordination and Workflow Gaps7 minutes
AI as Clinical Decision Support8 minutes
AI in Clinical Documentation and Triage7 minutes
Human-in-the-Loop AI Models7 minutes
What AI Can and Cannot Do8 minutes
Risks of Over-Automation7 minutes
Building Trust in AI Systems9 minutes

2 readingsTotal 10 minutes

Welcome to the Course: Course Overview5 minutes
AI in Healthcare: Opportunities and Limitations5 minutes

1 assignmentTotal 20 minutes

Clinical Challenges and the Role of AI in Healthcare20 minutes

1 peer reviewTotal 10 minutes

Hands-On-Learning: Mapping Clinical Problems to AI Solutions10 minutes

2 discussion promptsTotal 15 minutes

Which Clinical Pain Point Should Be Solved First?5 minutes
What Would Make You Trust or Question an AI Recommendation?10 minutes

This module is centered on the application of AI in the various areas of clinical diagnosis, patient care, and healthcare operations. The learners can understand the role of AI in clinical decision support, patient monitoring, and operational efficiency without the replacement of clinical judgment. The practical application of AI is demonstrated in this module through examples, which illustrate the interaction of clinicians and healthcare teams with AI systems, the interpretation of AI output, and the incorporation of AI output into healthcare operations.

What's included

10 videos1 reading1 assignment1 peer review2 discussion prompts

10 videosTotal 75 minutes

Module Introduction6 minutes
AI in Medical Imaging and Diagnostics8 minutes
Predictive Analytics and Risk Scoring8 minutes
Interpreting AI Outputs in Clinical Decisions9 minutes
AI for Patient Monitoring and Alerts7 minutes
AI Supported Personalized Care Pathways6 minutes
AI in Patient Engagement and Communication6 minutes
AI for Scheduling and Resource Optimization9 minutes
Clinical Documentation and Automation7 minutes
Measuring Operational Impact of AI10 minutes

1 readingTotal 5 minutes

AI Use Cases in Clinical Practice5 minutes

1 assignmentTotal 20 minutes

Applying AI Across Diagnostics, Patient Care, and Clinical Operations20 minutes

1 peer reviewTotal 10 minutes

Hands-On-Learning: Evaluating AI Use Cases in a Clinical Workflow10 minutes

2 discussion promptsTotal 10 minutes

Where Would Decision Support Help You Most?5 minutes
Balancing Automation with Human Care in Daily Workflows5 minutes

This module examines the successful implementation of AI and digital technology in the clinical setting. The learners will examine the changes that need to occur in the organization and workflow to implement AI in the clinical setting. This module highlights the importance of change management and building trust among clinicians to implement AI in the clinical setting. By the end of the module, the learners will have gained insight into the implementation of AI-driven digital transformation in the clinical setting.

What's included

10 videos1 reading1 assignment1 peer review2 discussion prompts

10 videosTotal 76 minutes

Module Introduction6 minutes
What Digital Transformation Really Means in Healthcare7 minutes
Common Reasons AI Projects Fail in Healthcare7 minutes
Aligning AI with Clinical Goals6 minutes
Identifying AI Touchpoints in Clinical Workflows9 minutes
Human–AI Collaboration Models9 minutes
Maintaining Clinical Accountability9 minutes
Clinician Adoption and Trust in AI8 minutes
Training and Upskilling Clinical Teams7 minutes
Measuring Adoption and Transformation Success8 minutes

1 readingTotal 5 minutes

Digital Transformation in Healthcare Explained5 minutes

1 assignmentTotal 20 minutes

Managing Digital Transformation and AI Adoption in Clinical Settings20 minutes

1 peer reviewTotal 10 minutes

Hands-On-Learning: Redesigning a Clinical Workflow for AI Integration10 minutes

2 discussion promptsTotal 10 minutes

Where Could AI Fit Naturally in Your Workflow?5 minutes
What Is the Biggest Barrier to AI Adoption in Your Setting?5 minutes

This module covers the ethical, governance, and responsibility issues that are critical to the adoption of AI in a healthcare setting. The learners will explore the impact of issues such as bias, transparency, accountability, and patient trust on the adoption of AI in a healthcare setting. The module also covers the governance and risk management practices that will enable organizations to adopt AI in a responsible manner. At the end of this module, the learners will have the perspective to balance innovation with ethical and patient-centered care.

What's included

10 videos1 reading1 assignment2 peer reviews2 discussion prompts

10 videosTotal 73 minutes

Module Introduction5 minutes
Fairness, Bias, and Patient Equity8 minutes
Transparency and Explainability in Clinical AI7 minutes
Accountability and Clinical Responsibility9 minutes
AI Governance in Healthcare Organizations7 minutes
Data Privacy and Patient Consent7 minutes
Risk Assessment and Clinical Safety7 minutes
Building Responsible AI Culture7 minutes
Preparing Clinicians for AI-Driven Futures7 minutes
Balancing Innovation with Patient Trust9 minutes

1 readingTotal 5 minutes

Ethical AI in Healthcare5 minutes

1 assignmentTotal 30 minutes

Ethical, Regulatory, and Responsible AI in Clinical Practice30 minutes

2 peer reviewsTotal 70 minutes

Hands-On-Learning: Ethical Risk Assessment of a Clinical AI System10 minutes
Project: Designing a Responsible AI Adoption Plan for a Clinical Setting60 minutes

2 discussion promptsTotal 10 minutes

What Safeguard Would You Prioritize First?5 minutes
How Should Healthcare Balance Innovation with Responsibility?5 minutes

Instructors

Ashish Mohan

5 Courses1,926 learners

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