In the world of foster care, caseworkers play a critical role in making sure that children end up in permanent, safe homes. A caseworker is often a child’s main point of contact and source of security, so it can be extremely disruptive if children have to switch caseworkers. In a case study conducted by Milwaukee County, children who have one caseworker achieve permanency 74.5 percent of the time. With two caseworkers, the chance of permanency decreases to 17.5 percent. This drop-off is dramatic, and without consistent caseworker care, children are at risk for worse outcomes in a system they did not choose.
Thankfully, organizations like Community Based Care of Central Florida (CBCCF) are working to provide this consistent support for children. Named by Orlando Sentinel as a top 100 company in Central Florida and the highest ranked non-profit, CBCCF acknowledges annual case manager turnover is still high, with an average around 40% and that they want to do something to change that turnover rate. DataKind and the Microsoft Cities Team joined up with CBCCF’s Director of Business Analytics and Automation, Matt Baker, to see if data science and machine learning could play a role in supporting caseworkers. The teams found two ideal opportunities to ease caseworkers’ day-to-day operations: in reducing the caseload burden on caseworkers and by automatically routing them to their visits quickly and efficiently.
First, the teams worked to ease the amount of heavy cases caseworkers had to deal with. CBCCF noticed that caseworkers could get burned out easily if they had too many large, complex cases on their plates, but it was hard for supervisors to estimate how taxing a case would be in advance of giving it to a caseworker. By compiling historical case data, however, the data science teams were able to build a computer algorithm that could predict how many hours a new case would take. Now, instead of adding complex cases to already overburdened caseworkers, managers can estimate the difficulty of a new case and distribute the cases effectively to prevent caseworkers from burning out.
Secondly, CBCCF and DataKind experts built algorithms to make it easier for caseworkers to plan their countless activities for the week. For every case, caseworkers have to schedule meetings, plan their route through counties, and try to time their day so that they’re meeting the children and their caregivers when they’re likely to be available. All too often, this exhausting process can waste huge amounts of time in planning and driving to different locations, sometimes retracing routes, and even going to the same location multiple times a week. The team built a tool that lets a caseworker enter their appointments, along with constraints, such as the order in which foster children need to be visited and the time windows in which those visits need to occur. With this tool, caseworkers can easily plan and visualize their routes, thereby diminishing the time and frustration of scheduling their week and safeguarding the time available to spend with the children and caregivers.
A screenshot of the DataKind tool for optimizing caseworker schedules.
These tools will now be used by CBCCF to better support and deploy caseworkers. “I have seen route optimization make a 30 mile difference for a day’s worth of activities. That can save hours a day and $2,025/day in travel costs alone for 150 case managers,“ said Matt Baker, Director of Business Analytics and Automation. CBCCF is also using the caseload algorithm to get current average caseload for caseworkers to be more balanced for them to spend more time working with the children and families they serve.
“Supporting our caseworkers so that they can be most efficient and effective is a top priority for CBCCF”, says Baker. “These hard-working individuals are on the front line of working with children in the foster care system. Removing barriers and frustrations for them is critical to allowing them to focus on what is most important: the vulnerable children entrusted to our care. The advances now available to us through DataKind are most important because they immediately and profoundly impact lives.”
These tools were built using the Bing Maps API and hosted sensitive caseworker data on Microsoft Azure servers. Those interested in keeping up with and building on this work can find more about the approach, results, and the resources that will be made public on Github via the project case study that will be shared on the DataKind blog soon. Stay tuned!