The decision-support layer for value-based care - built to answer the most expensive question in medicine: where does the patient go next?
The logo: four arrows pulling toward the center. Coordination, drawn as a picture. Concord, Massachusetts.
A case manager stands in a hallway with a chart in one hand and a phone in the other. The patient in bed 7 is an 82-year-old recovering from a fall. The clock on value-based contracts is ticking. The default is easy and expensive: send her to a skilled nursing facility. The better answer - can she safely heal at home with a little help? - is buried in thousands of datapoints no human can weigh in the ninety seconds available.
This hallway is where Radial lives. Not in a lab, not in a dazzling diagnostic demo, but in the unglamorous, high-stakes moment where care actually gets routed. The Concord, Massachusetts company builds decision-support software that reads the chart, the claims history, and the quality data, then hands the clinician a real-time, individualized answer. It does not overrule anyone. It nudges.
"We decided to start with critical 'crossroads moments' in a patient's care journey." - Thaddeus Fulford-Jones, Co-Founder & CEO
That word - crossroads - is the whole thesis. Most of healthcare's cost and most of its risk concentrate at a handful of transition points. Get those right and everything downstream improves. Radial's founders, two MIT PhDs who had already built and sold a location-analytics startup called Locately, recognized the pattern: they had spent years modeling where people go. Now they pointed the same instinct at the one journey nobody had mapped well - the patient's path after discharge.
Radial started with a single sharp product and grew a toolkit around it. Each module answers a different flavor of the same question a payer or provider faces under a risk-bearing contract.
Identifies, in real time, which patients can be safely discharged home with services instead of to a skilled nursing facility - using machine learning and NLP across clinical, claims, and quality data.
Surfaces patients appropriate for hospice and end-of-life care pathways, so the right conversation happens at the right time.
Lets accountable care organizations measure their cost and quality performance against peers - turning abstract benchmarks into concrete targets.
Ranks the highest-impact care and cost-savings opportunities so teams spend attention where it moves the needle.
Deliver real-time clinical guidance at the exact crossroads moment a decision is being made.
Operationalize evidence-based transitions and support CMS bundled-payment and value-based contracts.
Millions of clinical, claims, and quality datapoints flow in, cleaned and connected across sources.
Machine learning and NLP weigh each patient's individual risk of a bad transition - readmission, decline, unnecessary stay.
At the crossroads moment, the clinician sees a clear, individualized recommendation - not a verdict.
Outcomes feed back in: fewer readmissions, shorter stays, more care safely delivered at home.
"We believe in the transformative potential of AI - not to replace the decision-making authority of clinical staff, but to empower teams to make faster, smarter decisions." - Radial
Fulford-Jones and Weiss met as MIT PhD students - backgrounds in aerospace biomedical engineering and health sciences - and previously built Locately, later acquired. Dr. Vasudevan, a practicing hospitalist, brought the view from the bedside.
Hospitals, health systems, Medicare Advantage and other payers, and accountable care organizations operating under value-based contracts. Named customers and partners span some of the country's larger systems:
Return to bed 7. The case manager still has a chart in one hand and a phone in the other, and the clock still ticks. What has changed is the ninety seconds. Instead of defaulting to the expensive, institutional answer, she sees an individualized recommendation drawn from millions of comparable journeys: this patient, with this profile, can most likely go home safely - with these services in place.
She still makes the call. That is the point Radial keeps insisting on. The software did not diagnose, did not dazzle, did not replace her. It moved a better option to the front of a tired mind at the moment it mattered. The 82-year-old goes home. The readmission that might have followed a needless nursing-home stay never happens. The value-based contract quietly works the way it was always supposed to.
That is Radial's whole ambition, and it is a modest-sounding one for a company built on eighteen million patients' worth of data: make the invisible decision a little more visible, and let the human keep making it. In an industry loud with promises to remove people, betting on people turns out to be the radical move. The name fits.
Interviews & product-demo videos: search "Radial Analytics Smart Placement" on YouTube - the founders have discussed value-based care and post-acute decision support in health-tech panels.