Advances in technology and analytics can help state human-service agencies focus more on client families. Further, transformational models can make data accessible to clients and caseworkers alike, helping achieve potentially life-changing outcomes in efficient and cost-effective manners.
Although its core mission is to improve the trajectory of people’s lives, human services has long been more transactional than transformational.
For most human services programs, the business day consists of programmed actions and reactions, inputs and outputs, moving back and forth among government workers, their data systems, and their clients. In executing complex human services policies, success is defined primarily by the timeliness and accuracy of these transactions rather than their results. This has led to a model in which outcomes are in fact merely outputs: Did we issue food stamps in a timely fashion? Did we respond to 95 percent of our hotline calls within 24 hours?
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Rather than identifying and addressing the problems that bring individuals and families into contact with the social safety net, human services programs instead tend to see people through the lens of eligibility: Clients are enrolled in eligible programs, which means there is a particular set of services they can receive, even if those might not be the ones they really need to improve their situation. This program-centric view is a lingering byproduct of the way human services programs were originally created—as stand-alone programs rather than as an integrated safety net.
Thanks to advances in technology and analytical techniques, human services agencies are now poised to move beyond transactional service delivery. If agencies can put their data in front of both clients and caseworkers in a way that they can readily understand, and in time use the data in a way that affects results, then what was once a transactional business model can become a transformational one, capable of achieving potentially life-changing outcomes in an efficient and cost-effective manner. Instead of executing mundane tasks, social workers can focus on families and the help they need.
Advances in technology are significantly reducing manual processes and the need to manipulate reams of paperwork. These advances can free up caseworkers to focus their time and attention on providing specialized case management for clients, rather than becoming enmeshed in what they need to do to take care of transactional tasks and processes. Automation enables labor-saving innovations such as self-service eligibility portals, and back-end systems that can refer clients to services with little or no caseworker involvement. In some instances, outdated policies haven’t kept pace with new technologies, such as allowing for electronic correspondence with photo attachments or tele-interviews, which are more easily automated than paper-based processes.
Robotic process automation (RPA) technologies automate repeatable, rules-based tasks. Unlike a typical automated system function, RPA software, also known as a “bot,” operates at the user interface level and mimics the activities of a caseworker as it interacts with multiple applications in the execution of a task.
Take the foster family application process, in which repetitive tasks can eat up hours. Imagine having a bot take a scanned foster family application, enter it into the appropriate system, and even validate in a separate system to determine if a mandatory lead inspection was completed in the home. This not only frees up the caseworker to spend more time determining if the home meets quality expectations, but also retrieves the lead inspection information without needing to build a data link to a separate system.
This is just one example. The challenge is to look for low-risk, high-volume, repetitive tasks that traditionally take valuable time away from the caseworker and support staff, and give those tasks to the bot.
Just as businesses break their larger customer populations into subgroups with similar characteristics, human services programs too can segment their client bases. The goal is to deliver the right services to the right people. By rethinking the design and delivery of programs, human services agencies can better understand the diverse spectrum of needs among individual citizens and families. This can move human services systems from a “one-size-fits-all” approach to a “right-size-for-all” way of thinking about customers and what they need.
Enhanced data collection, coupled with the proliferation of agile and inexpensive technologies, is allowing for the increased use of analytics. This shifts the focus of human services from “hindsight” to “foresight and insight,” which can offer unprecedented opportunities for efficiencies and cost savings. It can also make sure that the right solutions get to the right people at the right time.
The introduction of artificial intelligence (AI) can bring big changes to human services agencies, freeing caseworkers to focus on life-changing work. AI can also help them to do a better job, providing the insights necessary to do the right work, for the right people, at the right time, thus achieving meaningful results for the individuals and families they serve.
To make the most of AI investments, agencies should consider redesigning their talent strategies so that a job is viewed not as an individual production function, but rather as a collaborative problem-solving effort, where a human defines the problems, machines help find the solutions, and the human verifies the acceptability of those solutions.5 Chatbots are another way to provide clients with smart guidance on questions about eligibility and policy, improving accuracy without tying up human resources. In addition, digital workflows can also augment worker impact through data analytics and behavioral “nudging.”
The Florida Child Support Program uses a predictive model to select compliance actions that will produce the best return on investment (ROI), bringing in the most collection money when compared with the costs. The model is based on two specific parameter groups—the financial compliance levels of cases and the indicators of the parents’ ability to pay (criminal history, employment, institutionalization status, and disabilities). For each case, the system then identifies the best course of action, selecting and prioritizing actions from a catalog of 11 possibilities. This minimizes the chance of using an expensive remedy, such as contempt, which requires activity by attorneys, in cases where that option is not likely to result in payment.
Caseworkers today often must manually verify beneficiaries’ eligibility by fetching data from multiple systems. In San Diego County, for example, caseworkers use two different systems for eligibility verifications. The first stores all the required documents to verify eligibility. The second has 500 different application forms; each form, or combination of forms, requires different documents.
Because these two systems don’t share information, caseworkers had to open forms from one system and then look for supporting documents in the other. Since there are 500 forms, these requirements create hundreds of business rules, which a caseworker had to verify manually. The process was complex and consumed a great deal of time.6
To automate the process and connect both systems, the county deployed RPA software. It looks at the open forms on a caseworker’s screen, sifts through the verification fields, identifies relevant documents, and then pulls up those documents from the other system. The entire manual task was replaced with the stroke of a hot key. Thanks to RPA, the county slashed the time it takes to approve a SNAP application from 60 days to less than a week.7
