Written by: Toria Thompson
H.R. 1, the federal budget reconciliation act signed in July 2025, requires states to verify that Medicaid expansion enrollees meet new work, education, or community involvement requirements, using existing data wherever possible rather than new paperwork. That approach, ex parte verification, works by linking payroll records, SNAP and TANF data, and claims data so the state can confirm compliance or exemption without the enrollee doing anything.
It also has a known failure mode. Ex parte verification only works for people whose lives show up cleanly in administrative data, such as steady payroll records, an active SNAP case, or a job reported the way the system expects. It does not work as well for someone whose hours fluctuate week to week, who is caring for a family member and qualifies for an exemption that no database tracks, who recently lost a job and hasn’t shown up in a new payroll file yet, or who qualifies but the records simply have not caught up. Those are not edge cases. They are a predictable share of the population, and the people in it are most likely to lose coverage not because they are ineligible, but because the data-matching systems missed them.
Closing that gap is not a data engineering problem. It is a question of who notices when someone falls out of the automated match, and what happens next. That is where community-based organizations, providers, and people who already have relationships with these enrollees come in. They can catch what ex parte cannot: flagging an exemption before a renewal deadline passes, helping someone document hours a payroll feed does not capture, or knowing when a member has gone quiet and following up before their coverage lapses.
Why a community design studio, and why not the data matching itself
California has already faced a version of this translation problem. Senate Bill 352, signed in September 2023, required a similar shift from statute to practice for reproductive health data sharing. The response was the AB 352 Toolkit, built by a working group of reproductive health providers, health systems, technology vendors, policy advocates, and lawyers. The group used data flow diagrams built around fictitious personas to surface the specific points where data sharing broke down or put someone at risk, and then designed directly around those points.
That format fits the community-level problem in H.R. 1: notification of a person’s trusted care provider at the point where an intervention can make all the difference. The large-scale data matching between state systems, health plans, and payroll or SNAP databases is an infrastructure build: procurement, system integration, compliance review. A community design studio does not replace that work. It’s suited for the harder, more human layer on top of that: mapping, case by case, where a community-based organization (CBO) or provider needs to intervene, what triggers that intervention, and what a warm handoff back into the verification process actually looks like. Health plans, providers, and county partners each see a different slice of who is falling through the cracks in the data-matching systems, and none of them can map the full pattern alone.
A community design studio built around personas representing the people who will fall through the cracks of ex parte produces something the statute cannot: a working sense of where community interventions need to sit in the process and what it takes to make those interventions timely enough to matter.
Learn more about the potential and impact of community design studios in operationalizing complex policy directives.