AI+ Medical Assistant™
Revolutionize Healthcare Support with AI-Powered Medical Assistance
This course develops patient interaction excellence by exploring how AI enhances communication, appointment scheduling, and follow-up care to improve the overall patient experience. It strengthens clinical workflow efficiency through AI tools designed to streamline patient intake, medical record management, and lab result analysis. Learners will gain data-driven decision support skills, understanding how AI assists healthcare providers with accurate diagnostics, treatment recommendations, and patient monitoring. The course also builds expertise in enhanced medical administration, preparing participants to support healthcare teams with AI-driven administrative tasks that reduce errors, improve accuracy, and enable faster decision-making.
Enrollment Fee:
MUR 5,980
What You'll Learn
1.1 Understanding AI and Its Healthcare Applications
1.2 The Role of AI in Medical Assistance
1.3 Case Studies
1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application
2.1 Healthcare Data Types and Management
2.2 Using Data Effectively in AI
2.3 Case Studies
2.4 Hands-On Session: Structured vs. Unstructured Data in Healthcare: A Practical Study Using Eka.Care Patient Health Record System
3.1 Enhancing Patient Interactions with AI
3.2 Predictive Analytics and Workflow Management
3.3 Case Studies
3.4 Hands-On Session: Eka.care in Action: Appointment Management, Smart Reminders & Tele-Consult Dashboards
4.1 Foundations of NLP for Medical Assistants
4.2 Practical Applications and Risks
4.3 Case Studies
4.4 Hands-On Simulation Exercise
4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows
5.1 Diagnostic Support Tools
5.2 Real-World Applications and Simulation
5.3 Use Cases
5.4 Hands-On: AI-Powered Detection of Common Health Conditions: Review and Analysis of AI-Suggested Diagnostic Insights using Eka Care
6.1 Recognizing and Addressing Bias in AI
6.2 Legal, Ethical, and Compliance Frameworks
6.3 Hands-On Exercise: Analyzing and Visualizing Bias in Artificial Intelligence Systems — Exploring Racial, Socioeconomic, and Demographic Disparities using Google’s What-If Tool
7.1 Selecting and Planning for AI Adoption
7.2 Best Practices and Stakeholder Engagement
7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
7.5 Hands-On Exercises: Evaluating the Relevance and Effectiveness of AI Models using the Zoho Analytics
8.1 Cybersecurity Risks and Protection
8.2 Future Trends and Preparing for Innovation
8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets
Prerequisites
- Basic Medical Terminology: Familiarity with healthcare concepts and terminology.
- Foundational Knowledge in AI: Understanding of machine learning and algorithms.
- Data Analytics Skills: Ability to analyze and interpret medical data.
- Programming Skills: Proficiency in Python or similar languages for AI tools.
- Understanding of Healthcare Systems: Knowledge of clinical workflows and medical practices.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 5,980