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AI+ Medical Assistant Practitioner™

Course code: NPAP5010

Formerly known as AI+ Medical Assistant™

Revolutionize Healthcare Support with AI-Powered Medical Assistance

Formerly known as AI+ Medical Assistant™
Revolutionize Healthcare Support with AI-Powered Medical Assistance

Patient Interaction Excellence: Learn how AI enhances patient communication, appointment scheduling, and follow-up care to improve the patient experience.
Clinical Workflow Efficiency: Master AI tools for streamlining patient intake, medical record management, and lab result analysis to optimize clinical operations.
Data-Driven Decision Support: Gain expertise in using AI to assist healthcare providers with accurate diagnostics, treatment suggestions, and patient monitoring.
Enhanced Medical Administration: Prepare to support healthcare teams with AI-driven administrative tasks, reducing errors, improving accuracy, and enabling faster decision-making.
75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date		 Place
Type Course
duration		 Language Price without VAT
G Upon request Self-paced 8 hours en 285 EUR Register
G Upon request Self-paced 8 hours en 75 EUR Register
G Guaranteed course

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Course description

AI Integration in Patient Care

Learn to integrate AI tools to assist with patient interaction, appointment scheduling, and follow-up care coordination.

Optimizing Clinical Workflows with AI

Gain expertise in using AI to streamline clinical tasks such as medical record management, data entry, and lab result analysis.

Enhancing Diagnostic Assistance with AI

Understand how AI-driven diagnostic support tools can aid in clinical decision-making and improve patient care outcomes.

Using Natural Language Processing (NLP) in Healthcare

Learn how to apply NLP to interpret and organize patient data from medical records, enabling better data management and insights.

AI-Driven Patient Monitoring and Coordination

Master AI tools for remote patient monitoring and improving patient coordination, ensuring real-time health status updates and seamless communication.

Course structure

Module 1: Fundamentals of AI for Medical Assistants

  • 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

Module 2: Data Literacy for Medical Assistants

  • 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

Module 3: AI in Patient Care Optimization

  • 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

Module 4: NLP and Generative AI in Medical Documentation

  • 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

Module 5: AI in Diagnostics and Screening

  • 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

Module 6: Ethics, Bias, and Regulation in AI for Healthcare

  • 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

Module 7: Evaluating and Implementing AI Tools

  • 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

Module 8: Cybersecurity and Emerging Trends in AI

  • 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

Optional Module: AI+ Medical Assistant Practitioner

  • 1.1 What Are AI Agents?
  • 1.2 How Does an AI Agent Work in Medical Assistance?
  • 1.3 Core Characteristics of AI Agents
  • 1.4 Importance of AI Agents in Healthcare
  • 1.5 Significance for Patient Experience & Clinical Outcomes
  • 1.6 Types of AI Agents
  • 1.7 Applications and Trends
  • 1.8 Case Study — AI Clinical Documentation at Mayo Clinic

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.

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