Business Application of Machine Learning and Artificial Intelligence in Healthcare

Instructor: Craig Johnson

Duration:

Learn how to integrate Machine Learning and Artificial Intelligence into the healthcare industry for improved bottom-line impact. Identify the best use cases for AI in healthcare and maximize your return on investment. ▼▲

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Coursera

Certificate:

Paid Certification

Language:

English

Start Date:

17th Jul, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [April 17th, 2023]

What skills and knowledge will you acquire during this course?
The course will provide students with the skills and knowledge to determine the factors involved in decision support that can improve business performance across the provider/payer ecosystem. Students will also learn how to identify opportunities for business applications in healthcare by applying journey mapping and pain point analysis in a real-world context. Additionally, students will gain the ability to identify differences in methods and techniques in order to appropriately apply them to pain points using case studies. Lastly, students will learn how to critically assess the opportunities to leverage decision support in adapting to trends in the industry.

How does this course contribute to professional growth?
This course contributes to professional growth by providing knowledge and skills in integrating Machine Learning and Artificial Intelligence into the healthcare industry. It helps leaders in the healthcare industry determine the best use of AI applications to solve problems that impact the bottom line. The course covers decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into healthcare.

Is this course suitable for preparing further education?
This course is suitable for preparing further education as it focuses on the business application of Machine Learning and Artificial Intelligence in healthcare. It aims to help leaders in the healthcare industry determine the best use for these technologies and focus their investment on solving problems that impact the bottom line. The course covers topics such as decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry. By the end of the course, students will be able to determine factors involved in decision support, identify opportunities for business applications in healthcare, identify differences in methods and techniques, and critically assess opportunities to leverage decision support in adapting to industry trends.

Pros & Cons

Pros Cons

Interesting topic and basics covered
Good examples
Innovative technology and management
Informative for beginners
Emphasis on business uses
Solid course

Repetitions
Assignments not reviewed on time
Instructor depersonalised
Examples could be better

Course Provider

Provider Coursera's Stats at AZClass

The future of healthcare increasingly depends on our ability to integrate machine learning and artificial intelligence into organizations. But simply recognizing the opportunity for AI is not enough; as leaders in healthcare, they must first identify the best uses for these applications to ensure they focus their investments on solving problems that impact the bottom line. Across these four modules, they will examine decision support, journey mapping, the use of predictive analytics, and the embedding of machine learning and artificial intelligence in the healthcare industry. You will be able to identify factors involved in decision support that can improve business performance across the provider payer ecosystem. Identify opportunities for healthcare business applications by applying journey mapping and pain point analysis in the real world.