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SCHEDULE
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With an exciting line-up of engaging topics and speakers, you will not want to miss anything! Do you have a busy day, or an upcoming deadline? We get it. That is why the Zoom event is an open-house format with sessions beginning on the hour with 15-minute breaks in between. You can even sit down with us during lunchtime for an informative panel discussion. |
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Click the time to see what session is happening:8:30 | 9:00 | 10:00 | 11:00 | 12:00 | 2:00 | 3:00 |
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Join us as we "dig for gold" in sizeable textual data sets to uncover the stakeholder themes and sentiment, with industry-standard accuracy rates. Using a presentation and demo, we will share the challenges we overcame to minimize text interpretation bias by using Natural Language Processing and Machine Learning (ML). Attendees will journey through our data mining process, which maximized model accuracy when identifying themes and associated sentiment. We will compare two thematic/sentiment model visualizations to emphasize the criticality of Human-Centered Design in big data interpretation while highlighting interactive filters to maximize targeted views of subsets of data. |
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9:45 - 10:00 | BREAK |
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When talking about Artificial Intelligence (AI) in the past few years, we have focused a lot on Machine Learning (ML), but much like in our school system, learning is only half of the equation. We are missing the other half: Machine TEACHING, which is where the human-centered portion comes in. This session will reveal the findings and intersect a technical discussion with a real business context: Fraud, Waste, and Abuse (FWA). For a business to thrive, cutting expenses is just as necessary as increasing income. In industries like Insurance, Finance, and Healthcare, FWA is one of the top costs, costing companies billions of dollars. Traditionally, a group of experts and investigators handle this time-consuming and labor-intensive process, but the rise of AI can assist humans in identifying and preventing FWA. This session will show not only “why” we should use AI to fight against FWA but also “how” to achieve it practically in 3 significant sections:
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10:45 - 11:00 | BREAK |
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Artificial intelligence (AI) seems to promise wide-ranging automation of many familiar civic technology components by providing just-in-time help to website visitors, performing audits and calculations, and personalizing services to specific users. However, in practice, these “artificially intelligent” systems generally rely on complex combinations of humans and machines to produce user experiences. This requirement poses unique challenges for service design and system transparency, with profound consequences for overall user trust and user experience. In this presentation, we take a sometimes-speculative look at how we have been thinking about “heteromated” systems such as chatbots and measure calculation on Healthcare Quality Reporting and beyond, providing both a theoretical overview and some practical guidance for UX professionals working with artificially intelligent systems. |
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11:30 - 12:00 | BREAK |
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Presenters: Keith McFarland (moderator) and panelists: Combiz Abdolrahimi 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 implementation 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
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1:15 - 2:00 | BREAK |
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Health data requires unique privacy and governance protections. Certain types of health data warrant specific protections based on how and from whom the data is collected. Patient-reported outcomes measures (PROs/PROMs) data, for example, require specificparticular protections, particularly when it is used in or informed by machine learning regimes. A patient/human-centered, federated learning architecture is appropriate for ensuring the privacy of users’ data. However, using a federated learning approach to provide privacy without attention to private data management dimensions may compromise users’ data unexpectedly. Users of machine learning to collect and manage PROs/PROMs should ensure that:
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2:45 - 3:00 | BREAK |
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This session will serve as a practical walk-through of combining techniques from Human-Centered Design (HCD) into Machine Learning (ML) projects to create usable, trustable, and accurate systems to support real-world data quality and data governance efforts. The presentation will connect the dots between the:
The presentation is targeted at cross-functional teams and provides an example implementation approach and common pitfalls seen along the way. The presentation will utilize a case study focused on improving data quality pipelines and supporting data governance – but the information applies to any HCML project. The audience will leave with:
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3:45 - 4:00 | Closing Remarks |
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