The $150 Billion Problem Hiding in Missed Medical Appointments

A $150 billion access problem

Roughly 3.6 million Americans miss medical care every year simply because they can't get to it, and that transportation gap is estimated to cost the U.S. healthcare system around $150 billion annually in missed appointments, delayed treatment, and preventable emergency visits, according to research compiled by the Medical Transportation Access Coalition.

Transportation barriers are estimated to account for roughly a quarter of all missed clinic appointments. One piece of research found that every dollar spent on non-emergency medical transportation generates around eleven dollars in downstream healthcare savings, largely through avoided ER visits and better chronic disease management. Ride-based interventions have been shown in some studies to meaningfully improve appointment attendance, though results vary and not every trial has replicated that effect, which is itself a sign of how much room there is for better operational tooling in this space.

The operational shape of NEMT

NEMT and home healthcare have almost the same operational shape as food delivery, just with far higher stakes attached to a late arrival. Orders become scheduled visits or rides. Drivers become caregivers or transport vehicles. Zones become care territories, often built around vulnerable, less mobile populations rather than restaurant density.

Predicting a late arrival before it happens

ATLAS's risk model was built to flag orders headed for trouble before they happen, using driver history, time-of-day, and live traffic. Applied here, the same architecture flags a scheduled visit or ride at risk of running late for an elderly or medically vulnerable patient, using caregiver or driver history, live traffic conditions, and territory load, the same inputs, pointed at a much higher-consequence outcome.

Current NEMT tooling largely relies on GPS tracking to catch a late arrival after the vehicle is already behind schedule. A risk-scored queue, the same one already running in the Dispatch Command Center for food delivery, catches the pattern before the vehicle leaves.

Zone-level patterns in missed visits

The zone model solves a real operational gap here too: care territories with a high concentration of missed or delayed visits are exactly the kind of pattern that's invisible stop-by-stop and obvious once measured at the zone level, the same disruption index that already flags under-supplied food delivery zones.

And because ATLAS was built with fault attribution baked into its incident data from day one, an operator running this model could actually distinguish a missed visit caused by a scheduling gap from one caused by traffic, a no-show, or an equipment failure, rather than lumping every missed appointment into a single no-show bucket the way most systems do today.

Higher stakes, same architecture

The overlap with pharma compliance and SLA management is direct: both come down to a small number of contact-time-critical events for vulnerable patients where being late isn't a minor inconvenience, it's a health outcome.