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.
提供方
課程信息
學生職業成果
50%
50%
您將學到的內容有
Determine the factors involved in decision support that can improve business performance across the provider/payer ecosystem
Identify opportunities for business applications in healthcare by applying journey mapping and pain point analysis in a real world context
Identify differences in methods and techniques in order to appropriately apply to pain points using case studies
Critically assess the opportunities to leverage decision support in adapting to trends in the industry
您將獲得的技能
學生職業成果
50%
50%
提供方

美国东北大学
Founded in 1898, Northeastern is a global research university with a distinctive, experience-driven approach to education and discovery. The university is a leader in experiential learning, powered by the world’s most far-reaching cooperative education program. The spirit of collaboration guides a use-inspired research enterprise focused on solving global challenges in health, security, and sustainability.
教學大綱 - 您將從這門課程中學到什麼
Decision Support and Use Cases
Rapid changes in technology are impacting every facet of modern society, and the healthcare industry is no exception. Navigating these changes is crucial, whether you are currently working in the industry, hoping to step into a new role, or are simply interested in how technology is being used in healthcare. No doubt you have heard the terms, “machine learning” and “artificial intelligence” more frequently in the last few years - but what does this mean for you, or the healthcare industry in general? Keeping up with the changing trends, examining the potential use of decision support, and identifying some of the pain points that can be addressed, are some of the topics we’ll be discussing in this Module.
Predictive Modeling Basics
Let’s navigate through what it takes to predict health outcomes and cost. What if we could use machine learning in your organization to reduce the cost of care for both the organization and the members receiving that care? Have you thought about what data you need to collect? How you might need to enrich that data to gain more insight in to what is driving those outcomes and cost? Or what types of machine learning algorithms you might utilize in order to most effectively target patients who are likely to be high cost? We are going to look at not only the tech behind the predictions, but also examine the business and data relationships within the healthcare industry that ultimately impact your ability to deliver an effective solution.
Consumerism and Operationalization
Now that we have discussed various types of predictive models, let’s 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.
Advanced Topics in Operationalization
Now that we know the importance of decision support and predictive modeling, we are going to take that one step further. Not only do we need to predict, but more importantly, we need to prescribe. It is not enough to just implement alerts and reminders - we need to offer guidance and recommendations for healthcare professionals. Let’s take a look at how analytics can improve the patient experience and their overall health status.
審閱
來自BUSINESS APPLICATION OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN HEALTHCARE的熱門評論
Solid course with an emphasis on the business uses rather than details of ML or AI.
Really informative for a beginner. A nice complement to my technology background.
Craig was too good in explaining the models with good examples
Excellent course for technology professionals in Healthcare.
關於 Healthcare Trends for Business Professionals 專項課程
This Specialization will provide learners with the knowledge and skills to recognize key shifts in the industry and to have an agile perspective on how these shifts might impact their organizations. Learners will be exposed to the key drivers in the global healthcare industry today so they might apply what they have learned to help their organizations.

常見問題
我什么时候能够访问课程视频和作业?
我订阅此专项课程后会得到什么?
Is financial aid available?
完成课程后,我会获得大学学分吗?
還有其他問題嗎?請訪問 學生幫助中心。