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Craig Johnson
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/26lnnt/business
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Syllabus
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.
Read more
Syllabus
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the integration of Artificial Intelligence and Machine Learning into modern organizations, especially through the lens of healthcare
Taught by Craig Johnson, an expert in the integration of technological solutions and modern healthcare
Explores methods for leveraging Artificial Intelligence and Machine Learning to improve decision-making, operational efficiency, and personalized healthcare interventions
Covers up-to-date applications of Artificial Intelligence and Machine Learning in the healthcare sector, including practical industry use cases
Suitable for healthcare professionals, technology administrators, and graduate students interested in the intersection of technology and medical practice
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Reviews summary
Informative healthcare business technology course
Learners say this informative course is excellent for technology professionals in healthcare. They appreciate the business focus and the course's relevance to the current healthcare landscape. However, one student had a negative experience with assignment review.
Emphasis on business applications.
"Solid course with an emphasis on the business uses rather than details of ML or AI."
Applicable to current healthcare challenges.
"The course is amazing considering the learning it offers and the application of that learning."
"I strongly believe that innovation in technology and management in healthcare holds dire importance in the current scenario and the course explains how we can leverage technology to address the underlying challenges and bring about a positive impact on our society."
Issues with assignment review turnaround.
"no one can reviewed my assignment from last 30 days"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Business Application of Machine Learning and Artificial Intelligence in Healthcare with these
activities:
Review Statistics and Probability
Show steps
Refresh foundational knowledge in statistics and probability to strengthen understanding of decision support concepts.
Browse courses on
Statistics
Show steps
-
Review key concepts in descriptive and inferential statistics.
-
Practice solving probability problems related to healthcare scenarios.
Review healthcare industry trends and challenges
Show steps
Build context and understanding of the healthcare industry landscape, which is essential for effective decision support.
Browse courses on
Healthcare Industry
Show steps
-
Read industry reports, articles, and white papers
-
Attend webinars and conferences to stay up-to-date on healthcare trends
Review decision support tools and methodologies
Show steps
Deepen foundational understanding of decision support tools and methodologies in advance of taking the course.
Browse courses on
Decision Support
Show steps
-
Research different decision support tools and their applications
-
Read white papers and case studies to understand the benefits and challenges
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Review machine learning research papers
Show steps
Stay up-to-date with the latest developments in machine learning and its applications in healthcare.
Browse courses on
Machine Learning
Show steps
-
Identify reputable scientific journals and conferences
-
Search for research papers that are relevant to the course topics
-
Read and summarize the key findings of the papers
Decision Support Algorithm Exercises
Show steps
Reinforce understanding of decision support algorithms through repetitive practice.
Browse courses on
Healthcare Analytics
Show steps
-
Solve practice problems involving decision trees, logistic regression, and Naive Bayes.
-
Analyze the results and identify patterns to improve algorithm selection.
Predictive Modeling Techniques
Show steps
Explore and practice various predictive modeling techniques to enhance understanding.
Browse courses on
Predictive Modeling
Show steps
-
Review online tutorials on supervised and unsupervised learning algorithms.
-
Implement these algorithms using open-source libraries (e.g., scikit-learn).
-
Apply the techniques to healthcare datasets to gain practical experience.
Introduction to Machine Learning with Python
Show steps
Gain a foundational understanding of machine learning concepts and Python implementation.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
-
Read Chapters 1-3 to grasp the basics of machine learning.
-
Complete the exercises in Chapter 4 to practice data preprocessing.
-
Implement a simple machine learning model using the code examples in Chapter 5.
Practice interpreting healthcare data
Show steps
Develop strong data analysis skills that are essential for effective decision support.
Browse courses on
Data Analysis
Show steps
-
Use real-world healthcare datasets to practice data cleaning and transformation
-
Conduct exploratory data analysis to identify key patterns and trends
-
Build predictive models using appropriate machine learning algorithms
Collaborate on a decision support project
Show steps
Engage in peer-to-peer learning by working on a decision support project with other students.
Browse courses on
Decision Support
Show steps
-
Form a team with other students who have similar interests
-
Brainstorm ideas and develop a project proposal
-
Collaborate on research, data analysis, and tool development
-
Present your findings to the class
Case Study Analysis
Show steps
Collaborate with peers to analyze real-world case studies and identify opportunities for leveraging decision support.
Browse courses on
Predictive Analytics
Show steps
-
Form study groups of 3-4 members.
-
Select a case study from provided materials or research relevant topics.
-
Analyze the case study, identify pain points and potential solutions using predictive analytics.
-
Present findings to the group for discussion and feedback.
Attend a workshop on advanced decision support techniques
Show steps
Acquire advanced knowledge and skills in decision support through a structured workshop setting.
Browse courses on
Decision Support
Show steps
-
Research and identify relevant workshops in the field
-
Attend the workshop and actively participate in discussions
-
Network with experts and professionals in the healthcare industry
Decision Support Tool
Show steps
Develop a tool to assist in decision-making for healthcare practitioners.
Browse courses on
Decision Support
Show steps
-
Identify a specific healthcare decision-making scenario.
-
Gather data and consult with healthcare professionals to understand the factors involved.
-
Design and build a machine learning model to predict outcomes based on the gathered data.
-
Create a user-friendly interface for the tool.
-
Validate the tool's accuracy and effectiveness through testing.
Develop a decision support tool
Show steps
Apply knowledge and skills to build a practical decision support tool that addresses a real-world healthcare problem.
Browse courses on
Decision Support
Show steps
-
Identify a specific healthcare problem or opportunity that can be addressed by a decision support tool
-
Design the tool's architecture, user interface, and functionality
-
Develop and implement the tool using appropriate technologies
-
Test and evaluate the tool's performance and impact
Healthcare Data Analytics Project
Show steps
Apply knowledge and skills to a real-world healthcare data analytics project in a volunteer setting.
Browse courses on
Healthcare Analytics
Show steps
-
Identify volunteer opportunities at healthcare organizations or research institutions.
-
Participate in data collection, analysis, and interpretation under the guidance of experienced professionals.
-
Contribute to the development of data-driven solutions for healthcare challenges.
Contribute to an open-source decision support project
Show steps
Gain practical experience and make direct contributions to the advancement of decision support.
Browse courses on
Decision Support
Show steps
-
Identify an open-source decision support project that is aligned with your interests
-
Review the project's codebase and documentation
-
Contribute to the project by adding new features, fixing bugs, or providing documentation
AI in Healthcare Hackathon
Show steps
Participate in a hackathon to develop innovative AI-powered solutions for healthcare challenges.
Browse courses on
Artificial Intelligence
Show steps
-
Form a team of 3-5 individuals with diverse skills.
-
Brainstorm and develop an AI-based solution to a healthcare problem.
-
Build a prototype and present it to a panel of judges and industry experts.
Review Statistics and Probability
Show steps
Refresh foundational knowledge in statistics and probability to strengthen understanding of decision support concepts.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review key concepts in descriptive and inferential statistics.
- Practice solving probability problems related to healthcare scenarios.
Review healthcare industry trends and challenges
Show steps
Build context and understanding of the healthcare industry landscape, which is essential for effective decision support.
Browse courses on
Healthcare Industry
Show steps
Show steps
Show steps
- Read industry reports, articles, and white papers
- Attend webinars and conferences to stay up-to-date on healthcare trends
Review decision support tools and methodologies
Show steps
Deepen foundational understanding of decision support tools and methodologies in advance of taking the course.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Research different decision support tools and their applications
- Read white papers and case studies to understand the benefits and challenges
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Review machine learning research papers
Show steps
Stay up-to-date with the latest developments in machine learning and its applications in healthcare.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify reputable scientific journals and conferences
- Search for research papers that are relevant to the course topics
- Read and summarize the key findings of the papers
Decision Support Algorithm Exercises
Show steps
Reinforce understanding of decision support algorithms through repetitive practice.
Browse courses on
Healthcare Analytics
Show steps
Show steps
Show steps
- Solve practice problems involving decision trees, logistic regression, and Naive Bayes.
- Analyze the results and identify patterns to improve algorithm selection.
Predictive Modeling Techniques
Show steps
Explore and practice various predictive modeling techniques to enhance understanding.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Review online tutorials on supervised and unsupervised learning algorithms.
- Implement these algorithms using open-source libraries (e.g., scikit-learn).
- Apply the techniques to healthcare datasets to gain practical experience.
Introduction to Machine Learning with Python
Show steps
Gain a foundational understanding of machine learning concepts and Python implementation.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to grasp the basics of machine learning.
- Complete the exercises in Chapter 4 to practice data preprocessing.
- Implement a simple machine learning model using the code examples in Chapter 5.
Practice interpreting healthcare data
Show steps
Develop strong data analysis skills that are essential for effective decision support.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Use real-world healthcare datasets to practice data cleaning and transformation
- Conduct exploratory data analysis to identify key patterns and trends
- Build predictive models using appropriate machine learning algorithms
Collaborate on a decision support project
Show steps
Engage in peer-to-peer learning by working on a decision support project with other students.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Form a team with other students who have similar interests
- Brainstorm ideas and develop a project proposal
- Collaborate on research, data analysis, and tool development
- Present your findings to the class
Case Study Analysis
Show steps
Collaborate with peers to analyze real-world case studies and identify opportunities for leveraging decision support.
Browse courses on
Predictive Analytics
Show steps
Show steps
Show steps
- Form study groups of 3-4 members.
- Select a case study from provided materials or research relevant topics.
- Analyze the case study, identify pain points and potential solutions using predictive analytics.
- Present findings to the group for discussion and feedback.
Attend a workshop on advanced decision support techniques
Show steps
Acquire advanced knowledge and skills in decision support through a structured workshop setting.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Research and identify relevant workshops in the field
- Attend the workshop and actively participate in discussions
- Network with experts and professionals in the healthcare industry
Decision Support Tool
Show steps
Develop a tool to assist in decision-making for healthcare practitioners.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Identify a specific healthcare decision-making scenario.
- Gather data and consult with healthcare professionals to understand the factors involved.
- Design and build a machine learning model to predict outcomes based on the gathered data.
- Create a user-friendly interface for the tool.
- Validate the tool's accuracy and effectiveness through testing.
Develop a decision support tool
Show steps
Apply knowledge and skills to build a practical decision support tool that addresses a real-world healthcare problem.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Identify a specific healthcare problem or opportunity that can be addressed by a decision support tool
- Design the tool's architecture, user interface, and functionality
- Develop and implement the tool using appropriate technologies
- Test and evaluate the tool's performance and impact
Healthcare Data Analytics Project
Show steps
Apply knowledge and skills to a real-world healthcare data analytics project in a volunteer setting.
Browse courses on
Healthcare Analytics
Show steps
Show steps
Show steps
- Identify volunteer opportunities at healthcare organizations or research institutions.
- Participate in data collection, analysis, and interpretation under the guidance of experienced professionals.
- Contribute to the development of data-driven solutions for healthcare challenges.
Contribute to an open-source decision support project
Show steps
Gain practical experience and make direct contributions to the advancement of decision support.
Browse courses on
Decision Support
Show steps
Show steps
Show steps
- Identify an open-source decision support project that is aligned with your interests
- Review the project's codebase and documentation
- Contribute to the project by adding new features, fixing bugs, or providing documentation
AI in Healthcare Hackathon
Show steps
Participate in a hackathon to develop innovative AI-powered solutions for healthcare challenges.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Form a team of 3-5 individuals with diverse skills.
- Brainstorm and develop an AI-based solution to a healthcare problem.
- Build a prototype and present it to a panel of judges and industry experts.
Career center
Learners who complete Business Application of Machine Learning and Artificial Intelligence in Healthcare will develop knowledge and skills
that may be useful to these careers:
Chief Data Officer
Chief Data Officer
Chief Data Officers oversee the data strategy and analytics functions of an organization. They are responsible for ensuring that the organization's data is used effectively to make informed decisions. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare provides a strong foundation in the use of machine learning and AI in the healthcare industry. This knowledge is essential for Chief Data Officers who want to stay ahead of the curve and use data to drive innovation in the healthcare sector.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. They work in a variety of industries, including healthcare, finance, and technology. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare provides a solid foundation in the use of machine learning and AI in the healthcare industry. This knowledge is essential for Data Scientists who want to work in the healthcare sector.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work in a variety of industries, including healthcare, finance, and technology. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare provides a strong foundation in the use of machine learning and AI in the healthcare industry. This knowledge is essential for Machine Learning Engineers who want to work in the healthcare sector.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use their knowledge of data analysis and healthcare to improve the quality and efficiency of healthcare delivery. They work in a variety of settings, including hospitals, clinics, and insurance companies. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare provides a solid foundation in the use of machine learning and AI in the healthcare industry. This knowledge is essential for Healthcare Data Analysts who want to stay ahead of the curve and use data to drive innovation in the healthcare sector.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide advice to healthcare organizations on a variety of topics, including strategy, operations, and technology. They work with healthcare organizations to improve their performance and achieve their goals. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare provides a strong foundation in the use of machine learning and AI in the healthcare industry. This knowledge is essential for Healthcare Consultants who want to stay ahead of the curve and advise their clients on how to use data to drive innovation in the healthcare sector.
Medical Researcher
Medical Researcher
Medical Researchers conduct research to improve the prevention, diagnosis, and treatment of diseases. They work in a variety of settings, including universities, hospitals, and research institutes. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Medical Researchers who want to use machine learning and AI to develop new treatments and improve patient outcomes.
Health Policy Analyst
Health Policy Analyst
Health Policy Analysts analyze health policy and make recommendations to improve the health of the population. They work in a variety of settings, including government agencies, think tanks, and advocacy organizations. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Health Policy Analysts who want to use machine learning and AI to develop new policies and improve the efficiency of healthcare delivery.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze studies in the health sciences. They work in a variety of settings, including universities, hospitals, and research institutes. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Biostatisticians who want to use machine learning and AI to develop new statistical methods and improve the efficiency of clinical trials.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states and events in specified populations. They work in a variety of settings, including universities, government agencies, and non-profit organizations. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Epidemiologists who want to use machine learning and AI to develop new methods for tracking and preventing diseases.
Medical Writer
Medical Writer
Medical Writers create written materials about medical topics for a variety of audiences, including patients, healthcare professionals, and the general public. They work in a variety of settings, including hospitals, clinics, and pharmaceutical companies. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Medical Writers who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve healthcare.
Pharmacist
Pharmacist
Pharmacists dispense medications and provide advice on their use. They work in a variety of settings, including pharmacies, hospitals, and clinics. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Pharmacists who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve the safety and efficacy of medications.
Nurse
Nurse
Nurses provide care to patients in a variety of settings, including hospitals, clinics, and long-term care facilities. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Nurses who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve patient care.
Physician Assistant
Physician Assistant
Physician Assistants provide medical care under the supervision of a physician. They work in a variety of settings, including hospitals, clinics, and long-term care facilities. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Physician Assistants who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve patient care.
Dentist
Dentist
Dentists provide dental care to patients of all ages. They work in a variety of settings, including private practices, clinics, and hospitals. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Dentists who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve dental care.
Veterinarian
Veterinarian
Veterinarians provide medical care to animals. They work in a variety of settings, including private practices, clinics, and animal shelters. The course Business Application of Machine Learning and Artificial Intelligence in Healthcare may be useful for Veterinarians who want to learn about the latest advances in machine learning and AI and how these technologies are being used to improve animal care.
For more career information including salaries, visit:
OpenCourser.com/course/26lnnt/business
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Business Application of Machine Learning and Artificial Intelligence in Healthcare.
Provides a good introduction to the basics of machine learning and its applications in the healthcare industry.
Provides an overview of artificial intelligence and its applications in health data analytics.
Specifically geared at healthcare practitioners and leaders, this book offers a conceptual foundation of AI and ML in healthcare and examines the social and ethical implications.
Provides an overview of artificial intelligence and its applications in medicine.
Provides a comprehensive overview of deep learning algorithms and their applications in healthcare. It covers topics such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Provides a practical guide to machine learning for healthcare professionals. It covers topics such as data collection, data pre-processing, feature engineering, model selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/26lnnt/business
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Northeastern University
Instructor
Craig Johnson
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This course belongs to a
collection
called
Healthcare Trends for Business Professionals. It's comprised of four courses:
- Future Healthcare Payment Models
- Evaluating the Quality of Healthcare Delivery
- Healthcare Consumerism and Implication for Care Delivery
- Business Application of Machine Learning and Artificial Intelligence in Healthcare (viewing)
Good to know
Examines the integration of Artificial Intelligence and Machine Learning into modern organizations, especially through the lens of healthcare
Taught by Craig Johnson, an expert in the integration of technological solutions and modern healthcare
Explores methods for leveraging Artificial Intelligence and Machine Learning to improve decision-making, operational efficiency, and personalized healthcare interventions
Covers up-to-date applications of Artificial Intelligence and Machine Learning in the healthcare sector, including practical industry use cases
Suitable for healthcare professionals, technology administrators, and graduate students interested in the intersection of technology and medical practice
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