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Just as artificial intelligence is transforming the consumer experience, it, and other emerging technologies, can help health and human service agencies deliver better, faster services.
In the consumer realm, technologies based on artificial intelligence (AI) are slowly changing the way we manage everyday tasks. Take the driving app Waze, for example. Waze uses crowdsourced data, social networking conversations, and cognitive learning to help shave time off daily commutes by providing the most efficient route based on current conditions and individual driving preferences.
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Or consider products like Nest. Gone are the days of paying to heat or cool your house while no one’s home. When people are at home, Nest learns what temperature household occupants prefer at different times of the day, automatically adjusting home thermostats to help users save money on their energy bills.1
Meanwhile, intelligent personal assistants, such as Siri®2 and Alexa, have helped reduce the time required to carry out routine tasks such as turning off the lights or ordering dinner, enabling their users to refocus limited attention on higher-order tasks.3
Similar AI technologies can be put to use in human services, to help agencies cut costs, improve decision-making, free up significant labor hours for more critical tasks, and deliver better, faster services.
To better understand AI’s potential application in human services, it is critical to learn about some of the key automation and AI-based technologies that are impacting our lives. These technologies include robotic process automation (RPA), rule-based systems, machine learning, computer vision, speech recognition, natural language processing, and robotics (figure 1).
Government entities are putting these technologies to work today in an effort to reduce the administrative burden on caseworkers, address long wait times, triage high caseloads based on risk, and free up staff time to deal with more complex cases (figure 2).
Often caseworkers must manually verify the eligibility of beneficiaries by fetching data from multiple systems. Take San Diego County, for example. County caseworkers use two different systems for eligibility verifications. The first system stores all the required documents to verify eligibility. The second system has 500 different application forms, and each form or combination of forms requires different documents. The problem is that the two systems do not communicate with each other. As a result, caseworkers open forms from one system and then look for supporting documents in the other system. Since there are 500 forms, these requirements create hundreds of business rules, which a caseworker has to verify manually. The process is complex and consumes huge amounts of caseworkers’ time.4
To automate the process and connect both systems, the county deployed RPA software. The software automatically 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 a single keystroke. As a result of using RPA, the time it takes to approve a Supplemental Nutrition Assistance Program (SNAP) application was cut from 60 days to less than a week.5
Australia’s Department of Human Services (DHS) is using cognitive technologies to help reduce its staff workload. The department deployed an internal virtual assistant called Roxy to answer queries from case-processing officers related to the rules and regulations of the department’s programs. Roxy uses machine learning and natural language processing to understand human language and respond to requests.6 Roxy is currently responding to more than 78 percent of the questions being put to it.7 Prior to Roxy, DHS staff would call human experts for assistance. Now human experts only get involved in complex queries.8 According to former DHS chief technology officer Charles McHardie, "It's been quite successful at reducing their workload."9
Oklahoma’s Department of Human Services has used cognitive technology to help identify child welfare cases most likely to lead to child fatalities. The department partnered with Eckerd Kids, whose software uses machine learning to predict cases with a high probability of child fatalities. Factors such as a child under the age of three, intergenerational abuse, young parents, mental health problems, and a history of substance abuse tend to be correlated with a high risk of child fatalities.9 Once high-risk cases get flagged, they go through a detailed review, and the input is shared with front-line staff so that they can establish a course of action that abates risk and improves outcomes.11 This helps field staff target investigations based on risk rather than relying on random sampling.
Today, typical human services employees allocate their labor among a “basket” of tasks. By breaking jobs into individual activities and analyzing how suitable each is to automation, we can project the number of labor hours that could potentially be freed up by investing in AI-based technologies. Our analysis of human service agencies in a large Midwestern state found that automation could yield time savings of up to 34 percent. This amounts to 3 million hours freed up, yielding potential annual savings of US$73 million (figure 3). At the low end of the investment spectrum, automation could still save 305,000 hours annually, with potential savings of US$7 million.
AI-based technologies are already having a profound impact on consumers. These technologies could be further applied to human services programs to help reduce backlogs, cut costs, overcome resource constraints, free caseworkers to spend more time with families, inject intelligence into scores of processes and systems, and handle many other tasks humans can’t easily do on our own.
It’s highly unusual for a business improvement to increase speed, enhance quality, and reduce costs at the same time, but AI-based technologies offer that possibility. For human services workers, it could mean a future where vast amounts of their time are freed up thanks to automation, their effectiveness is enhanced, and their capabilities extended with machine learning.