MTM HealthPlatform, Data & Engineering.

Why this work matters

CMS describes non-emergency medical transportation as an important benefit for people who need help getting to and from medical appointments. The operation spans members, health plans, transportation providers, medical facilities, scheduling, dispatch, trip tracking, claims, and fraud controls.

In November 2021, MTM Health publicly announced that all nationwide trip and call volume had moved to MTM Link, its proprietary platform. The company described an end-to-end system covering trip entry, provider assignment, dispatch, visibility, self-service, and claims submission. That public milestone shows the operating scale surrounding the platform and analytics work during this chapter.

What was hard

I joined as MTM Health’s first data and analytics hire. The company coordinated transportation for more than one million members and over ten million trips a year, but data and software were not yet operating as a mature product and engineering function.

The opportunity was larger than reporting. A missed trip can mean a missed dialysis appointment, delayed medication, or a member losing confidence in the system. The operation needed to see problems early enough to act, not only explain them after the fact.

State Medicaid agencies and managed-care organizations also needed a clearer view of performance, utilization, cost, and member experience. Internal analytics could answer those questions only if we designed it as a repeatable product rather than a custom report for every client.

What I owned

I started with the operating decisions: Which trips were at risk? Which providers were likely to perform? Where was demand moving? What did an operations leader or client need to know in time to act?

My scope grew from establishing the data work to leading software engineering, analytics, platform strategy, member-facing products, and commercial product development.

I also built the engineering and data-science organization from zero to more than 30 people, including the managers, career paths, and operating cadence needed to keep the work growing after the original builders moved on.

What we built

The team built a real-time dispatching and analytics foundation using trip history, GPS signals, provider performance, and demand patterns. It helped the operation match supply to need, identify failure risk earlier, and see service performance with less delay.

We applied machine learning across millions of trip and contact-center records to move from retrospective reporting toward intervention. The useful output was not a model score by itself. It was a signal an operating team could understand and act on.

We then turned the analytics capability outward. I identified the gap in client reporting, built the business case and product model, and helped launch the company’s first commercial analytics product for Medicaid agencies and managed-care organizations.

What changed

Data moved from a reporting function to part of how the operation ran. The organization grew from the first analytics hire to a durable engineering and data-science function with its own leaders and product discipline.

Analytics also moved from an internal capability to a repeatable commercial product. That changed how the team framed the work: start with an operating problem, make the decision clearer, and build the result so it can serve more than one client or team.

What I learned

The best data products shorten the distance between a signal and a better decision. A model, dashboard, or platform matters only when someone can use it in the moment the operation can still change the outcome.

Building the team and building the product discipline were the same job. Both started with the person making the decision and worked backward from what they needed to know.