This workshop guide offers teams an opportunity to jointly work toward understanding core problems impacting digital delivery in their organization. The guide is structured in two parts: (1) a Miro template and (2) a Facilitation Guide.
This workshop guide offers teams an opportunity to jointly work toward understanding core problems impacting digital delivery in their organization. The guide is structured in two parts: (1) a Miro template and (2) a Facilitation Guide.
This interview template includes questions designed to help teams conduct exploratory, semi-structured interviews with government stakeholders involved in program delivery to gather information that can help them evaluate the status quo of digital delivery in their organization.
The article discusses effective strategies for training government partners in digital services, emphasizing the importance of prioritizing training, setting clear objectives, and fostering mutual understanding and trust.
For the Digital Service Network’s (DSN) final installment of its summer event series, Let’s Get Digital, we heard about New York State’s (NYS) human-centered design (HCD) journey and how relationships between leadership and digital service teams have been pivotal in advancing user-centric service delivery.
The second event in the Digital Service Network’s summer event series, Let’s Get Digital, focused on the City of Boston’s transformative journey to streamline its procurement processes.
The team explored using LLMs to interpret the Program Operations Manual System (POMS) into plain language logic models and flowcharts as educational resources for SSI and SSDI eligibility, benchmarking LLMs in RAG methods for reliability in answering queries and providing useful instructions to users.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
The team developed an AI-powered explanation feature that effectively translates complex, multi-program policy calculations into clear and accessible explanations, enabling users to explore "what-if" scenarios and understand key factors influencing benefit amounts and eligibility thresholds.
The team developed an AI solution to assist benefit navigators with in-the-moment program information, finding that while LLMs are useful for summarizing and interpreting text, they are not ideal for implementing strict formulas like benefit calculations, but can accelerate the eligibility process by leveraging their strengths in general tasks.
