AI Integration in Healthcare Patient Data

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AI Integration in Healthcare Patient Data

This course is part of Digital Healthcare Informatics and AI Specialization

Instructor: Frank Naeymi-Rad

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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This course is part of the Digital Healthcare Informatics and AI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

This course delves into the integration of artificial intelligence (AI) within healthcare, focusing on patient data management. Learners will explore the ethical and policy considerations surrounding AI applications in clinical and public health settings. The course emphasizes the significance of data quality and the potential impact of bias on clinical decision support systems. Participants will gain hands-on experience with AI and machine learning tools, including natural language processing (NLP) applications for clinical note summarization. Additionally, the course covers the integration of AI with electronic health records (EHRs) and clinical decision support systems, equipping learners with the knowledge to leverage AI for improved patient outcomes.

Discover the AI and machine learning tools transforming healthcare, including NLP for clinical notes, predictive analytics, and AI integration with EHRs.

What's included

22 videos3 readings2 assignments1 discussion prompt

22 videos Total 56 minutes

About the Specialization 2 minutes
Welcome and Course Overview 1 minute
Instructor Introduction 5 minutes
C3M1L1.1 Lecture Overview and Introduction to AI & ML in Healthcare 2 minutes
C3M1L1.2 Clinical Applications of AI & ML in Healthcare 2 minutes
C3M1L1.3 AI & ML in Research & Precision Medicine 3 minutes
C3M1L1.4 Generative AI in Healthcare 3 minutes
C3M1L1.5 Operational & Public Health Impact 4 minutes
C3M1L2.1 Lecture Overview and Natural Language Processing in Healthcare 2 minutes
C3M1L2.2 Case Study - CareScore AI – NLP Documentation for Medical Training 2 minutes
C3M1L2.3 Generative AI for Patient Engagement & Telehealth 3 minutes
C3M1L2.4 Case Study – AI-Enabled Telehealth for Heart Failure Management 2 minutes
C3M1L2.5 Dynamic Case Generation for Medical Training 3 minutes
C3M1L2.6 Case Study – AI Case Generator for Medical Education 2 minutes
C3M1L2.7 Integration with EHRs & Clinical Decision Support 2 minutes
C3M1L3.1 Lecture Overview and Introduction to AI Agents 1 minute
C3M1L3.2 Why Healthcare Needs AI Agents 3 minutes
C3M1L3.3 Single Agent vs Multi-Agent Workflows 3 minutes
C3M1L3.4 Core Coordination Mechanisms 2 minutes
C3M1L3.5 FIPA Standards and Enabling Mechanisms 2 minutes
C3M1L3.6 Example 1 - MedAgentSim: Multi-Agent Virtual Patient Simulation 3 minutes
C3M1L3.7 Example 2 - AI-Driven EHR Coordination Inspired by Multi-Agent Design 2 minutes

3 readings Total 80 minutes

Syllabus 10 minutes
Module 1 Overview 10 minutes
Module 1 Readings 60 minutes

2 assignments Total 95 minutes

Module 1 Check Your Knowledge 5 minutes
Generative AI vs. AI Agents for Workflow 90 minutes

1 discussion prompt Total 10 minutes

Meet & Greet 10 minutes

Explore the ethical and policy considerations for AI in healthcare, including regulatory requirements and guidelines. Understand the importance of high-quality data and how bias can impact clinical decision support and patient care.

What's included

28 videos2 readings2 assignments

28 videos Total 80 minutes

C3M2L1.1 Lecture Overview and Framing AI Ethics in Healthcare 2 minutes
C3M2L1.2 Ethical & Policy Vectors Driving Regulation 2 minutes
C3M2L1.3 FDA - Implications for Clinical AI 2 minutes
C3M2L1.4 HIPAA & Privacy Obligations in Ethical AI 2 minutes
C3M2L1.5 Research vs Clinical Use - IRB & IDE 2 minutes
C3M2L1.6 U.S. vs International Regulatory Approaches 3 minutes
C3M2L1.7 Real-World AI Governance- Mayo Clinic Model 3 minutes
C3M2L1.8 Why Compliance Adds Value 3 minutes
C3M2L2.1 Lecture Overview and Introduction to Bias & Fairness in a Dataset 3 minutes
C3M2L2.2 Fairness Metrics in Medical Datasets 4 minutes
C3M2L2.3 Common Types of Dataset Bias in Healthcare 4 minutes
C3M2L2.4 Sources of Healthcare Dataset Bias 2 minutes
C3M2L2.5 Documentation Bias and Workflow Effects 3 minutes
C3M2L2.6 Patient Care & Equity Outcomes 3 minutes
C3M2L2.7 Data Quality Fundamentals for AI Reliability 4 minutes
C3M2L2.8 Data Governance Framework 4 minutes
C3M2L2.9 Data Cleaning & Validation Methods 4 minutes
C3M2L2.10 Quality Checks and Monitoring Systems 3 minutes
C3M2L2.11 Mitigation Strategies and Best Practices 3 minutes
C3M2L3.1 Lecture Overview and Why Responsible AI Matters 2 minutes
C3M2L3.2 Institutional AI Governance Framework 3 minutes
C3M2L3.3 What is Explainability & Why It Matters 2 minutes
C3M2L3.4 Hierarchical Framework for AI Transparency 2 minutes
C3M2L3.5 Explainable AI Techniques 4 minutes
C3M2L3.6 Compliance in a Multi-Jurisdictional World 3 minutes
C3M2L3.7 Implementation - From Framework to Practice 2 minutes
C3M2L3.8 Measuring Responsible AI Implementation 2 minutes
C3M2L3.9 Emerging Trends in Responsible AI 3 minutes

2 readings Total 65 minutes

Module 2 Overview 10 minutes
Module 2 Readings 55 minutes

2 assignments Total 125 minutes

Module 2 Check Your Knowledge 5 minutes
AI System Risk and Bias Assessment 120 minutes

What's included

2 assignments

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Instructor

Frank Naeymi-Rad

Illinois Tech

4 Courses 1,336 learners

Offered by

Illinois Tech

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