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AI/ML for Frontline Healthcare Workers

Practical AI/ML Knowledge to Enhance Your Daily Work

Course Description

Designed with Frontline Healthcare Workers in mind, this asynchronous course offers a unique opportunity to unlock the potential of Artificial Intelligence (AI) and Machine Learning (ML) without the need for coding expertise. While many courses emphasize coding skills, our approach is different. We have identified the needs specific to frontline healthcare workers through a nationwide survey and targeted interviews to inform this curriculum. The resounding consensus is clear: you want practical knowledge that directly enhances your day-to-day work. 

No coding experience is required for this course. This course will guide you as you walk through real-world healthcare use cases applying cutting-edge technology and approaches. You will explore the world of AI/ML applications on healthcare and health equity to gain a deep understanding of the technology, capabilities, limitations, and potential impact on patient care. Join us in this transformative journey where technology meets healthcare expertise. Enroll today and become an AI/ML-savvy healthcare worker, empowered to make transformative differences in the lives of your patients. 

Intended Audience

While open to anyone eager to dive into this topic, we have thoughtfully designed this course with Frontline Healthcare Workers in mind. Frontline Healthcare Workers, as we have defined them for this course, are individuals who are employed at any worksite with direct patient care. This includes allied health professionals, supportive frontline staff, doctors, nurses, medical residents, dentists, nursing home managers, mental health providers, pharmacists, IT professionals engaged with the patients care, and other healthcare community members. Whether you are a Frontline Healthcare Worker or in a role that is closely related, this course has been tailored to provide an overview of AI and ML, demonstrating how these technologies may be applied directly to patient care.

Course Learning Objectives

This course is intended to provide frontline healthcare workers with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) and its application to healthcare and health equity. 

  1. Foundational Understanding: Develop a comprehensive understanding of AI and ML, their roles in healthcare, and the potential impact on healthcare workflows.
  2. Clinical Relevance: Explore how AI enhances patient care, aids in healthcare research and drug discovery, and addresses the pros and cons of AI in healthcare.
  3. Data in Healthcare: Examine various data sources in healthcare, including EHRs, medical imaging, and wearables, and understand the importance of data quality, privacy, and security.
  4. CDSS Proficiency: Gain proficiency in Clinical Decision Support Systems (CDSS), including their applications, integration into healthcare workflows, and methods for evaluating CDSS performance.
  5. Patient-Centric Focus: Learn about AI tools and technologies designed to improve patient engagement, develop effective communication strategies with patients involving AI, and recognize the importance of health equity in AI-powered healthcare.
  6. Future Trends: Explore emerging trends in healthcare AI/ML, gain exposure to trending use cases projects, and understand how AI is shaping the future of healthcare.

Course Structure

Module 1: Introduction to AI and ML in Healthcare

  • Lesson 1: Understanding AI and ML
  • Lesson 2: How AI Helps Patients
  • Lesson 3: AI in Healthcare Research and Drug Discovery
  • Lesson 4: Pros and Cons of AI in Healthcare

Module 2: AI Applications for Frontline Healthcare and Future Trends

  • Lesson 1: Clinical Impact of AI 
  • Lesson 2: Imaging and Diagnostics
  • Lesson 3: Clinical Decision Support Systems (CDSS
  • Lesson 4: Natural Language Processing (NLP) 
  • Lesson 5: Future Trends in Healthcare AI/ML

Module 3: Data Sources in Healthcare 

  • Lesson 1: Electronic Health Records (EHRs)
  • Lesson 2: Medical Imaging Data 
  • Lesson 3: Wearable and Remote Monitoring Data
  • Lesson 4: Data Quality, Privacy, and Security in Healthcare

Module 4: Ethics in AI/ML

  • Lesson 1: Data Privacy, Data Security, IRB, and Compliance
  • Lesson 2: Bias and Fairness in AI/ML
  • Lesson 3: Transparency, Explainability, and Accountability
  • Lesson 4: Social and Cultural Impacts

Module 5: Patient Engagement, AI, and Health Equity

  • Lesson 1: Patient-Centric AI Solutions 
  • Lesson 2: Effective Communication 
  • Lesson 3: Health Equity in AI-Powered Healthcare 
  • Lesson 4: Building Patient Trust in AI

Instrumental Persons

We would like to recognize and express our gratitude to the following people who shared in the creation and implementation of this course:

  • Chitra Nayak, PhD: Presenter
  • Dr. Jaime Smith: Presenter
  • Dr. Yohn Jairo Parra Bautista: Presenter
  • Robin Ghosh, PhD: Presenter
  • Stephen Fernandez, MPH: Presenter
  • Benjamin Collins, MD, MS, MA: Presenter
  • Nawar Shara, PhD: Course Director
  • Omar Aljawfi, PhD: Course Director, Curriculum Designer, Presenter
  • Alexander Libin, PhD: AI-LEARN Project Director, Frontline Healthcare Worker Needs Assessment Lead
  • Sara Stienecker, MS, MBA: Course Director
  • Prabhjeet Singh, BDS, MSPH: Course Director
  • Tom Whitfield: Course Director
  • Rose Yesha, PhD: Course Director, Course Curriculum Support, Presenter
  • Alejandro Manrique-Pinell: Research Intern, Course Coordinator
  • Toufeeq Ahmed Syed, PhD: Course Director
  • Aidan Hoyal, MSIS, MA: Communications Hub, AIM-AHEAD
  • Zainab Latif, MCS: Communications Hub, AIM-AHEAD 
  • Paul Warhurst, PhD: Communications Hub, AIM-AHEAD 

Funding

The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program is funded by NIH, Agreement No.: 1OT2OD032581-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.