Business Application of Machine Learning and Artificial Intelligence in Healthcare
- 4.1
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Approx. 12 hours to complete
Course Summary
Artificial Intelligence in Healthcare is a course that provides learners with the knowledge and skills to apply AI in healthcare, including machine learning, natural language processing, and robotics.Key Learning Points
- Understand the basics of AI in healthcare and its applications.
- Identify the challenges and opportunities in the implementation of AI in healthcare.
- Learn how to design and evaluate AI applications in healthcare.
Related Topics for further study
Learning Outcomes
- Ability to identify opportunities for AI applications in healthcare
- Skills to design and evaluate AI applications in healthcare
- Understanding of the challenges and limitations of implementing AI in healthcare
Prerequisites or good to have knowledge before taking this course
- Basic understanding of healthcare industry
- Familiarity with programming concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments
- Quizzes
Similar Courses
- AI for Medical Treatment
- AI for Medical Diagnosis
- AI in Financial Services
Related Education Paths
Related Books
Description
The future of healthcare is becoming dependent on our ability to integrate Machine Learning and Artificial Intelligence into our organizations. But it is not enough to recognize the opportunities of AI; we as leaders in the healthcare industry have to first determine the best use for these applications ensuring that we focus our investment on solving problems that impact the bottom line.
Knowledge
- Determine the factors involved in decision support that can improve business performance across the provider/payer ecosystem
- Identify opportunities for business applications in healthcare by applying journey mapping and pain point analysis in a real world context
- Identify differences in methods and techniques in order to appropriately apply to pain points using case studies
- Critically assess the opportunities to leverage decision support in adapting to trends in the industry
Outline
- Decision Support and Use Cases
- Course Overview
- Introduction to Module 1
- Consumerism, Supply Chain and Social & Situational Determinants
- Operationalizing Consumerism Using ML and AI
- Interview with Caitlyn
- Operationalizing a New Supply Chain
- Interview with Peter Dunphy
- Machine Learning, Artificial Intelligence, and Decision Support
- Journey Mapping and Pain Points
- Patient Monitoring
- Interview with Cait Larson from Dynamicare
- Differential Diagnosis
- Care Management
- Preventive Screening
- Avoidable Readmissions
- Healthcare Ecosystem Readings
- Healthcare Consumer Journey Mapping
- TED Talk on an innovation in Remote Patient Monitoring
- Innovations and Results in Patient Outreach
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Module 1 (Graded)
- Predictive Modeling Basics
- Introduction to Module 2
- Predictive Modeling
- Linear Regression
- Disease Burden as a Predictor of Cost
- Machine Learning
- Data Sourcing
- Data Enrichment
- Provider Taxonomies and Relationships
- Predictive Modeling Process
- Linear Regression Explained
- Using AI to Diagnose Disease
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Consumerism and Operationalization
- Introduction to Module 3
- Analytic Maturity Model
- Identifying Historic Addressable Opportunity
- Predicting Addressable Opportunity
- Measuring Predictive Accuracy
- Making Recommendations
- Voices from the Industry with George "Russ" Moran
- Integration and Orchestration
- Operational Engagement Framework
- The Future of Predictive Analytics in Healthcare
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Module 3 (Graded)
- Advanced Topics in Operationalization
- Introduction to Module 4
- Operational Entity Relationship Model
- Using Other Administrative Data to Target Avoidable Utilization
- Targeting High Value Member Patients Using Consumer Data
- Recommending a Program for Care Management
- Recommending a Channel for Member Engagement
- Interview with Peter Dunphy from Perfect Health
- Embedding Decision Support with your Existing Technology Footprint
- Deploying Decision Support Beyond the Enterprise to the Consumer
- Utilizing Consumer Data
- Misconceptions in the Industry
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
- Check Your Knowledge
Summary of User Reviews
Read reviews of Artificial Intelligence in Healthcare on Coursera. Discover what others have to say about this course and its overall rating. Learn about the pros and cons of this course from real users.Key Aspect Users Liked About This Course
Many users found the course to be very informative and well-structured.Pros from User Reviews
- Course content is comprehensive and covers a broad range of topics
- Instructors are knowledgeable and provide clear explanations
- Assignments and quizzes are challenging and engaging
Cons from User Reviews
- Some users found the course to be too technical and difficult to follow
- The course may not be suitable for beginners in healthcare or AI
- The course does not provide hands-on experience with AI tools and applications
Craig Johnson
4.1 Raiting
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