Imagine for a moment: You’re a single parent working to make ends meet as you balance rent, bills and the cost of living. Add on losing your job. Then, food assistance and health care access become incredibly important to your family.
Millions of households receive social safety benefits. In the US, there are more than 80 federal services available. It’s clear the system is overburdened. Compounding this are workforce challenges: the Silver Tsunami of Baby Boomers retiring and the struggle to attract a younger workforce to public sector jobs.
A simple approach to AI can make a big difference
Bringing in any computing automation can help optimize how governments assess eligibility and needs of welfare benefits, make enrollment decisions and manage benefit delivery. Many states are making technology investments – spending millions to replace legacy systems. But is the technology really augmenting their efforts? Governments need to consider simple AI applications.
First, walk before running. For AI to augment the work of caseworkers and make social programs more efficient, governments need to figure out the biggest pain points:
- Which tasks are manual or repetitive?
- What are the most complex processes?
- Which processes can be simplified or streamlined?
It’s human nature to dislike mundane work. And it’s an inefficient use of a caseworker’s time. This is where AI-based technology can make a difference. For instance, AI can augment the work of caseworkers by automating paperwork and helping them know which cases need urgent attention.
Let’s not forget about the relationship side of casework. Because of the high volume of cases, some states have moved to case banking to address staff shortages. The downside? The relationship aspect is lost. Caseworkers making the decisions don’t have a history that could help them determine eligibility. If the technology falls short of providing that history, the individual or family is impacted. AI augmentation could help the technology.
Safeguarding the nation's most important anti-hunger program
The Supplemental Nutritional Assistance Program (SNAP) is a vital service administered by local and state governments. Individuals and families rely on it, and they suffer the most when fraud and errors occur.
In November 2023, the federal government released its 2022 findings: More than 11 percent of cases were determined to be in error – representing millions of dollars that needed to be recovered. What factors contribute to this problem? Benefits amounts are determined monthly. Overpayments are identified monthly. Both are a manual process. A single overpayment can take days to calculate.
How can governments become more efficient? Automation. It’s one of the most common benefits of AI. Pair AI with advanced analytics and governments can lower error rates, catch overpayments before they happen and improve caseworker training on how to determine eligibility. In addition, they can better balance workloads using analysis and automate triage assignments.
More efficient benefit recovery
SAS helped a state government automate its overpayment determination and recovery processes. Rather than manually entering data from the eligibility system into spreadsheets, they were able to pull the data and prepare it for caseworker review. Data automation reduces errors, recalculates each affected month, identifies the total overpayment, and automatically pulls the organized data into the eligibility system.
Advantages for the agency include streamlining notifications to beneficiaries, reducing the process from days to hours, and decreasing errors from manual data entry. Overall job satisfaction is improved.
Improving data transfers with Medicaid-managed care organizations
Nearly 78 million individuals are enrolled in their state’s Medicaid program. That’s about 1 in 5 Americans who qualify as low-income children, adults, seniors or people with disabilities that rely on public health insurance.
Medicaid is complex and often susceptible to overpayment and billing errors. It becomes more complicated when states use managed care organizations (MCOs) to help administer the program. Why? Because of data collection and transfers. For instance, collecting the necessary data is mostly done manually, which is tedious and challenging for many providers. Also, audits of providers may include payments from a state agency and numerous MCOs.
Improved data transfer for better outcomes
With SAS, a state agency could automate repetitive data transfer between the agency and multiple MCO payers. Exchanging manual spreadsheets between the agency and the MCOs to conduct a provider audit was a time-intensive back-and-forth effort, often taking weeks to resolve a single audit.
Also, the manual process created opportunities for errors or incomplete data being provided to payers. With the automated review process, the data quality has improved, and it takes minutes rather than days to provide an audit list to payers. The agency’s staff can be more efficient and focus their efforts because mundane tasks are now automated.
Augment with analytics and AI
The potential of AI in social welfare programs could deliver incredible results that align with the government’s goal of ensuring the well-being of its people. AI-based technology can play a positive role in reducing errors and overpayments, making resource allocation decisions, improving employee satisfaction and providing services more efficiently to citizens.
In a previous blog, Data Analytics or Plowing a Field With a Fork? I shared how better analytics can make a tremendous difference in processes. I cited a use case where enhanced analytics reduced audit staff from 42 to eight – an 80 percent savings for the agency – which led to reassigning auditors to other social benefit areas that needed attention.
Take small steps at first. They will pave the path for your agency's digital transformation. Each success will lead you to explore more complex use cases and opportunities for improvement. Let's build a future where social programs thrive and communities flourish.
