8:30- 9:00 | Welcome from CMS Leadership
9:00 - 9:45 | A Gold Mining Adventure – Using Natural Language Processing and Machine Learning to find Gold in Unstructured Data Presenter: Chris Schilstra Presentation: Join us to "dig for gold" in sizeable textual data sets to uncover stakeholder themes and sentiment, with industry-standard accuracy rates. We will compare two thematic/sentiment model visualizations with a presentation and demo and explore the importance of incorporating Human-Centered Design (HCD) with Artificial Intelligence (AI). Areas of interest: Data, Design, Policy, Technology
9:45 - 10:00 | BREAK
10:00 - 10:45: Catch Me if You Can – How to Fight Fraud, Waste and Abuse using Machine Learning AND Machine TEACHING (by human) Presenter: Cupid Chan Plenary Session: Machine Learning (ML) is often the focus of an Artificial Intelligence (AI) discussion, but Machine TEACHING is just as important. This session will intersect a technical conversation with a real business context: Fraud, Waste, and Abuse. Areas of interest: this is the morning plenary session, and we encourage everyone to attend.
10:45 - 11:00 | BREAK
11:00 - 11:30 | How Humans Make AI Work Presenters: Ian Lowrie and Stephanie Warren Presentation: “Artificially intelligent” systems rely on complex combinations of humans and machines to produce the desired user experiences, posing challenges for service design and ultimately affecting overall user trust and user experience. This session will explore systems like chatbots and provide practical guidance for UX professionals working with or curious about Artificial Intelligence (AI). Areas of interest: Data, Design, Product, Technology
11:30 - 12:00 | BREAK
12:00 - 1:15 | Capabilities and Challenges for Machine Learning focused on Preserving Privacy and CMS Healthcare Goals Presenters: Keith McFarland (moderator) and panelists: Combiz Abdolrahimi, Steve Geller, Harlan Krumholz, MD, Darryl Marshall and Bin Shao, Ph.D. Panel Discussion: Machine Learning (ML) can support patient care improvement while managing costs, but there are risks involved. Join us for a panel discussion on how a ML approach can be implemented without compromising reliability, trustworthiness, and safety. The panel of professionals will share their knowledge in areas including Human-Centered Design (HCD), Health Privacy, Data, and more. Areas of interest: this is a panel discussion, and we encourage everyone to attend
1:15 - 2:00 | BREAK
2:00 - 2:45 | Federated Learning to Collect Mobile Patient-Reported Outcomes Presenters: Dr. Rachele Hendricks-Sturrup and Dr. Sara Jordan Plenary Session: Health data requires unique privacy and governance protections, and patient-reported outcomes measures (PROs/PROMs) data is no exception. We will discuss what it takes to ensure patient privacy in federated learning architectures. Areas of interest: this is our afternoon plenary session, everyone is encouraged to attend
2:45 - 3:00 | BREAK
3:00- 3:45 | Using Human-Centered Machine-Learning (HCML) to Improve Data Quality & Data Governance Projects Presenter: Edward F. O'Connor Presentation: Are you interested in understanding the components of a real-world and complex Machine Learning (ML) project? Join us as we walk through the implementation process of combining Human-Centered Design (HCD) techniques into a ML project. Areas of interest: Data, Design, Product, Strategy, Technology
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