This demo showcases a guided claims intake experience powered by a conversation agent that steers users through a structured claim case workflow. It highlights how Embedded Data and Data References enable clean, reusable data capture across single entries and repeated lists, while dynamic UI patterns like cascading dropdowns, calculated fields, and visibility conditions keep the experience intuitive and error-resistant.
Users start by confirming plan member details, then add treatment information with mixed data types and on-the-fly calculations. The claim type and subtype are selected through cascading dropdowns that reduce invalid choices. The flow supports multiple claimants and claim items via PageLists, and provider selection is streamlined with searchable references, both single and multi-select.
These features demonstrate how to build a resilient intake flow that reduces friction, enforces data quality, and accelerates submission, all while keeping the interface clean and responsive to user inputs
- Collecting the Plan Member details using Embedded Data( Single Page)
- Collect Treatment Details - Multiple Data types + Calculated fields + Visibility conditions
- Collect Claim Type and SubType using Cascading dropdowns
- Collect Claimant Details using Embedded Data (PageList)
- Collect Claim Details using Embedded Data (PageList)
- Select Physician detail using Data Reference (Single)
- Select Additional Physicians using Data Reference (PageList)
Reference: https://docs.pega.com/bundle/platform/page/platform/gen-ai/supported-fields-limitations-agents.html
https://players.brightcove.net/1519050010001/default_default/index.html?videoId=6387823926112
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