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.
Artificial Intelligence and Machine Learning (AI/ML), Emerging Technologies, Advanced Analytics, Healthcare Industry Knowledge, Process Analysis, Business Process Automation, Predictive Analytics, Business Analytics, Data-Driven Decision-Making, Business Intelligence, Decision Support Systems, Analytics, Customer experience strategy (CX), Predictive Modeling, Health Informatics, Applied Machine Learning
Reviews
4.4 (82 ratings)
5 stars
62.19%
4 stars
26.82%
3 stars
3.65%
2 stars
1.21%
1 star
6.09%
NN
Jan 28, 2020
Craig was too good in explaining the models with good examples
SB
Jul 10, 2020
Excellent course for technology professionals in Healthcare.
From the lesson
Consumerism and Operationalization
Now that we have discussed various types of predictive models, let鈥檚 take a look at which models are appropriate for the business case we are trying to address and how we can evaluate their performance. For example, is using the same performance metric appropriate to use when making predictions about individual vs. population health? In this module we'll discuss how layering appropriate decision support methods on top of predictive analytics and machine learning can lay the groundwork for significant improvements in overall outreach and productivity, as well as decrease costs. Finally, we will discuss the key to blending decision support into the existing ecosystem of your business workflow and technology infrastructure.