AI with Heart: Solving Real Problems with Compassion and Efficiency
Updated: Oct 8
—Our SNAP program dropped thousands of incoming phone calls last year, and our customer satisfaction scores reflect it—missed calls, no return calls, and a website that doesn’t offer the support our customers need.
—We are exceeding the 30-day requirement for processing SNAP applications, which means hungry people in our community are waiting too long for the benefits they desperately need.
Are these experiences familiar to you? They are common scenarios across the country.
The demand on Health and Human Services (HHS) agencies is rising, the effect of which is exacerbated by the expansion of the Supplemental Nutrition Assistance Program (SNAP) and Medicaid across the U.S. This surge has placed a strain on HHS agencies that are already experiencing high vacancy rates—20% or higher in some locations—for crucial staff who are responsible for determining benefit eligibility. This not only impacts individuals seeking benefit assistance but also contributes to burnout among agency workers striving to address their neighbors’ needs. Those seeking support face prolonged delays as agencies struggle to keep up. Without additional support, agencies will be unable to meet the escalating demand, worsening the disparities among vulnerable populations and hindering community well-being.
How AI Can Help
EVA is an AI-powered virtual assistant tailored to handle customer service and general information calls coming into HHS agencies. Its implementation offers a dual benefit: optimizing agency workforce and facilitating the efficient allocation of resources. This enables agency workers to concentrate on complex cases while ensuring timely and accurate services for those in need.
As an AI-powered tool that is available to external callers and chats all hours and all days (24/7/365), it provides consistent answers to questions based on federal, state, and local guidelines. Additionally, it recognizes and speaks multiple languages, breaking down the barriers to services experienced by non-English speaking callers—no more prompts to select a language, no more waiting on hold for a translator.
EVA is also available internally to workers for benefit eligibility questions, agency policy and procedure questions, and outside community services referrals when eligibility requirements aren’t met but a need still exists.
When a question is outside the scope of knowledge, such as with complex applications, a call is automatically transferred to a worker to handle, and all the information gathered is transferred too. Callers aren’t frustrated by the need to repeat themselves.
Furthermore, AI’s continuous learning capabilities are a vital tool for improving the overall system. From identifying and blocking spam calls to enhancing virtual training based on recorded topics from transferred calls, AI plays an active role in refining its own performance. The reporting of call statistics informs resource allocations, contributing to a more adaptive and responsive system.
Conclusion
The incorporation of AI in HHS agencies isn’t just a technological upgrade; it’s a testament to our commitment to compassion and efficiency. By leveraging AI, we have the opportunity to revolutionize the way we provide essential services, creating a more accessible, responsive, and equitable system for all. The path forward is clear—embracing AI is not just a choice; it’s a compassionate imperative that can redefine the future of public service.
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