AI Series: AI in Healthcare Capstone Project

Monday, November 16, 2020, 12:00 AM - Wednesday, November 15, 2023, 11:59 PM

Internet Enduring Material Sponsored by Stanford University School of Medicine. Presented by the  Center for Health Education at Stanford University School of Medicine. This capstone project course takes the learner on a guided tour exploring all the concepts that have been covered in the AI Series to date. This course is centered around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID-19, seeks care with a primary care provider. We follow the patient's journey through the lens of the data that is created at each encounter, which brings us to a unique de-identified dataset created specifically for this specialization. The data set spans EHR as well as image data. Using this dataset, we build models that enable risk-stratification decisions for our patient. We review how the different choices that are made -- such as those around feature construction, the data types used, the set-up of the model evaluation and patient timeline -- affect the care that is recommended by the model. During this exploration, we also discuss the regulatory and ethical issues that arise as we attempt to use AI to help us make better health care decisions for our patient. This course is a hands-on experience in the day of a medical data miner.

  Release Date: November 16, 2020
  Expiration Date: November 15, 2023
  Estimated Time to Complete: 11.0 hours

Click Begin (at the top) to learn more about how to enroll in the course. 

View entire series: https://www.coursera.org/specializations/ai-healthcare

AMA PRA Category 1 Credits™ (11.00 hours), Non-Physician Participation Credit (11.00 hours)

1. Identify a framework for conceptualizing data usage in healthcare.
2. Apply the fundamentals of AI and machine learning to a real world data problem.
3. Use state-of-the art techniques to tune and improve AI and machine learning techniques in healthcare.
4. Demonstrate a holistic approach to model evaluation of the clinical utility of AI and machine learning models in healthcare.
5. Analyze the value of deployment considerations and downstream evaluations for AI solutions in healthcare.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

Credit Designation

American Medical Association (AMA)
Stanford Medicine designates this Enduring Material for a maximum of 11.00 AMA PRA Category 1 Credits^TM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Cultural and Linguistic Competency
The planners and speakers of this CME activity have been encouraged to address cultural issues relevant to their topic area for the purpose of complying with California Assembly Bill 1195. Moreover, the Stanford University School of Medicine Multicultural Health Portal contains many useful cultural and linguistic competency tools including culture guides, language access information and pertinent state and federal laws.  You are encouraged to visit the Multicultural Health Portal: http://lane.stanford.edu/portals/cultural.html

Reference/Bibliography List
For additional resources, please visit the course.

For activity related questions, please contact
  Ph: 650.204.3984
  Email: [email protected]

For CME general questions, please contact 
   Ph: (650)-497-8554
   Email: [email protected]

The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. The content of this activity is not related to products or the business lines of an ACCME-defined commercial interest. Hence, there are no relevant financial relationships with an ACCME-defined commercial interests for anyone who was in control of the content of this activity.