Emerging Trends in Health and Human Services: It’s all about Artificial Intelligence (AI)

Health and Human Services has two major IT conferences, Medicaid Enterprise Systems Conference or MESC (covering Medicaid and Medicaid Claims Payment) and IT Solutions Management Conference or ISM (covering primarily human services and eligibility: Child Welfare, SNAP, TANF, and increasingly Child Support Enforcement). We’ve been participating at both these conferences and tracking the changing imperatives.

Being modular

In the last few years, MESC has focused on MES and modularity. That was true again this year. Increasingly, ISM has also been adopting a focus on modularity. Several states are taking a modular, platform approach to their eligibility systems. The bigger change was the new attention to AI and its role in operations and service delivery. Chat GPT has everybody buzzing about how AI will impact health and human services. While there will be an impact, it’s not in generative AI yet but refinement of chatbots, RPA, and predictive modeling – technologies under development for a while.

In the context of health and human services, large language models help customers interact with automated systems to fulfill their need – benefit information, updates to demographics, screening for new benefits, etc. – quickly and in a knowledgeable, friendly fashion. It is an extension of chatbot functionality. As AI increases the ability of machines to analyze “corner cases”, it shifts the workload for overburdened caseworkers to the machine. RPA started this process, AI will accelerate it. At some point, decisions normally made by humans, such as eligibility determinations, protective orders, or household interventions can be made by AI informed by large data sets. These tools will also be able to catch initial signs of problems before it becomes obvious an intervention must occur.

Effectively using AI: Lessons from the Past

AI can empower and extend the caseworkers’ capacity, but it is important to remember the lessons of the past. Predictive models were developed in the Child Welfare space that ended up with too many false positives and false negatives to be of use – kids were either taken unnecessarily or were not protected in a timely fashion.ⁱ The lesson is to be absolutely sure the model works before relying on it. And, it is important to note that today’s AI is known for bias.ⁱⁱ

Another important consideration is the long-term usefulness of the new AI technology. Blockchain was the cool, new technology a few years back. But health and human services struggled to build a use case for blockchain to be a tool that enabled new capabilities that can’t be replicated without it. Instead, we saw use cases that forced the use of blockchain to highlight the use of a cool, new technology for the sake of a cool, new technology.ⁱⁱⁱ The lesson is to make sure that AI makes sense to use.

Also Read: Future Vision for HHS Technology

Infosys has deep roots in new technology. We have evolved from an IT Services firm to an automation enabler, cloud enabler, and now an AI enabler. Historically, we enabled automation through products such as EdgeVerve.^iv We enabled, and continue to enable, cloud technology through our Cobalt offerings.^v Today, we are an AI enabler through our Topaz offerings.^vi Infosys Live Enterprise Application Management Platform (LEAP) is our unique tool that combines automation, cloud, and AI.^vii LEAP enables AI-driven automation of IT operations to provide maximum return for agencies’ IT investment along with best possible service delivery from their systems and infrastructure. LEAP is a practical synthesis of technologies that Infosys has created to enable the modern IT enterprise leverage AI more effectively.

The near future of AI in health and human services looks much like it does now, but with some refinements.

Author Details

Richard Brady

Rick leads the Government Healthcare practice at Infosys for the US and Canada. He has 25+ years experience in healthcare and public sector.

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