Building Rural Health Transformation That Outlasts the Funding

The Rural Health Transformation Program gives rural health systems something rare: a large, dedicated pool of money to redesign how they deliver care. It is $50 billion across the 50 states, $10 billion a year from federal fiscal 2026 through 2030, paid out through cooperative agreements with annual budget periods. For many systems, it is the biggest investment in their care model they will see in a generation. It also comes with a built-in deadline. The funding runs for five years and then ends. Whatever a system builds with it eventually has to stand on its own, supported by payers, employer contracts, operating margin, or a future appropriation that may or may not come.

That timeline shapes a question worth holding onto from the start: not only what to build, but how to show later that it worked.

Why the "show it worked" part is easy to underweight

When funding arrives, the natural focus is the visible gaps. A new access point. The care coordinator a panel has needed for years. A remote monitoring program. A behavioral health line. These are the things that show up in a budget narrative, and the things a community notices right away.

Measurement tends to sit lower on the list. It is less tangible, and its value shows up later rather than now. But it is what allows a system to answer a straightforward question a year or two in: did the program change anything, and is there evidence for it?

When the answer depends on data spread across several systems with no baseline recorded, a program may well have worked without anyone being able to demonstrate it. That gap can become a real constraint the moment a system looks for renewal funding, a payer contract, or an employer partnership.

Measurement is mostly a set of early decisions

It can help to think about measurement less as a tool bought after launch and more as a few choices made before it, while there is still room to make them.

One is defining what success means in advance. For each initiative, that means naming the outcome in measurable terms, ideally tied to the goals the program is organized around, such as chronic disease management, behavioral health access, or maternal and prenatal care.

Another is capturing a baseline before the program starts. Improvement is only visible against a starting point, and a baseline reconstructed after the fact tends to be incomplete.

A third is building data capture into the everyday work, in the visit, the intake, and the EHR, rather than collecting it in a separate effort near a reporting deadline. Data gathered in the flow of care is usually more complete than data assembled later.

A fourth is planning for more than one system from the outset. Rural networks rarely run on a single EHR. A small hospital, a few independent practices, an FQHC, and a new virtual care line may each use something different, and the ability to bring those records together is what turns site-level activity into a program-level picture.

The audiences that will eventually ask

Measurement is not only about internal reporting. It supports several later conversations, and each audience tends to look for different evidence.

States and CMS focus on accountability across budget periods and on whether the program's strategic goals moved, which matters for continued funding over the five years.

Payers, including Medicaid managed care, Medicare Advantage, and commercial plans, tend to weigh total cost of care, utilization, and quality measures when deciding whether to renew or expand a relationship. Employers, particularly where a system is doing direct contracting, generally look for the effect on plan spend and a return-on-investment view they can share internally.

Each of these is easier to navigate when the relevant measurement was in place from the beginning rather than assembled once the question came up.

The timing is what makes it worth doing now

Measurement is generally cheaper to build in than to add later. Reconstructing a couple of years of outcomes from scattered records costs more in staff time, and the result is rarely as clean as data captured as it happened. The practical window to set this up well overlaps with the window the funding is open. So alongside the sites, the staffing, and the technology, the measurement layer is worth real attention early. It is the part that lets a program show its value after the funding period, and it is much harder to add once that period is underway.

This layer is a large part of what we work on at Health Compiler: bringing data together across whatever systems a network runs, defining and tracking the measures that matter, and turning it into reporting that holds up with states, payers, and employers. If you are designing or redesigning a program under RHTP and want to think through the measurement side, we are happy to talk.