Northeastern University - Business Application of Machine Learning and Artificial Intelligence in Healthcare
- Offered byCoursera
- Public/Government Institute
Business Application of Machine Learning and Artificial Intelligence in Healthcare at Coursera Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Business Application of Machine Learning and Artificial Intelligence in Healthcare at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the Healthcare Trends for Business Professionals Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 12 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Business Application of Machine Learning and Artificial Intelligence in Healthcare at Coursera Course details
- 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.
- Throughout these four modules we will examine the use of decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry. By the end of this course you will be able to:
- 1. Determine the factors involved in decision support that can improve business performance across the provider/payer ecosystem.
- 2. Identify opportunities for business applications in healthcare by applying journey mapping and pain point analysis in a real world context.
- 3. Identify differences in methods and techniques in order to appropriately apply to pain points using case studies.
- 4. Critically assess the opportunities to leverage decision support in adapting to trends in the industry.
Business Application of Machine Learning and Artificial Intelligence in Healthcare at Coursera Curriculum
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
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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
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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
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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
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