ProjektSUBRO

Shyaam Nanthan — Sun, 15 Nov 2026 10:58:10 +0000

This is a submission for the MLH x DEV Writing Challenge.

What I Built

ProjektSUBRO connects upfront funding with automated recovery of denied insurance claims.

Healthcare providers need cash for staff, supplies, and patient care—even while insurers review payments for care already delivered.

Why this matters

Nearly $18 billion spent by U.S. hospitals fighting claim denials in 2025. AHA
41.2% of rural hospitals operated at a loss, and 417 were vulnerable to closure, according to Chartis’ 2026 analysis. Chartis
68.6% of denied claims were eventually paid in Premier’s survey of 2023 claims—but providers averaged three review rounds, each taking 45–60 days. Premier

SUBRO prices denied claims, simulates upfront funding, and runs recovery workflows. Evidence, agent actions, and financial outcomes stay visible in one workspace.

$$
\text{Expected profit} = (\text{Collectible} \times P(\text{recovery})) - \text{Advance} - \text{Costs}
$$

The demo uses 120 synthetic claims. No real patient data or money. Recovery probabilities are assumptions.

Demo

Try SUBRO live

Open Full recovery, inspect the claim, and approve the simulated advance. Follow the recovery run, then check Ledger to compare the forecast with the recorded outcome.

Other scenarios demonstrate missing evidence, expired deadlines, repeated rejection, conflicting records, and partial payment.

Partner Technologies

The current demo does not integrate MLH partner APIs.

I built it with Next.js, React, TypeScript, Tailwind CSS, Recharts, and Vercel. Deterministic functions handle pricing; a bounded recovery agent supports optional AI-assisted appeal drafting. Browser localStorage preserves the sandbox.

Hackathon Experience

I built SUBRO for SanD Hacks Fall 2026.

The hardest parts were preventing duplicate transactions, separating forecasts from outcomes, and deciding when recovery should stop.

My biggest takeaway: trustworthy automation depends on structured records, evidence, and guardrails. That shaped the audit trail and ledger—each action needed enough context to explain what happened and how it affected the money.