Find Local Help provides a list of free experts who can help users sign up for health insurance. The system asked additional questions before the results screen to give the backend system time to complete the search. Users received a “No Results” notification – instead of loading indicators – during this step.
Ad Hoc designers on the CMS Find Local Help team researched user pain points and used human-centered design to help deliver the needed information as quickly and simply as possible. The team found responses were taking two to six seconds because the location search didn’t use an index when returning results. Ad Hoc used query plans and R-Tree spatial indexes to reduce the average response time to 150 milliseconds. Ad Hoc removed the extra questions before the results screen.
In the original tool’s design, users received two lists of people who could help them: one for assisters and one for agents and brokers. For people who need one-on-one help, the priority was to get them one list of everyone who can help. Ad Hoc condensed the results into one page with explanations of the differences between available experts and simple filters so users see the list that’s right for them.
Project Outcomes and Impact
This made a difference for Find Local Help users by: - Prioritizing users in the research process - Using human-centered design approach - Deploying technical updates to speed up the search
CMS was able to better serve users and strengthen this critical step in the process of getting health insurance.
Conducting extensive research on user pain points and using human-centered design are simple approaches to creating new tools, but are ultimately extremely effective. It is possible to design government services that address users’ needs while mirroring the experience of consumer tools.