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In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?  Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health. Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis. In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more.  Links Mentioned in the Show: MultiplanPlanOptix: Providing Innovative Healthcare Price Transparency   Using a Data Mining Service on Claims Data Can Reveal Significant OverpaymentsConnect with Travis and JocelynCourse: Intro to Data PrivacyRelated Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at TruvetaRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r1ebfddfb7a40b4fbacd4e30bc006d29a

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r1ebfddfb7a40b4fbacd4e30bc006d29a

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r35a4b43d09dd200b049cf51dd4fce7c5

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r35a4b43d09dd200b049cf51dd4fce7c5