Score reimbursement likelihood before the device ships.
A DME startup was writing off revenue because nobody could predict which placements would get reimbursed. We built an AI engine that checks every case against Medicare and Medicaid policy before anything goes out the door.
Overview
Impact, measured in results
Of DME placed industry-wide fails reimbursement. That's the baseline we're fixing.
Per-patient reimbursement likelihood, checked before the device ships
Justification tied to specific Medicare and Medicaid rules, generated automatically
A healthcare startup in the durable medical equipment space, running patient programs that provide medical devices under Medicare and Medicaid coverage. Industry-wide, an estimated 20 to 50 percent of DME placed in the field fails reimbursement and gets written off.
Why AppStream
We used to guess. Now we know before we ship whether a placement will get paid.
Founder
DME Healthcare Startup
Train AI on real policy. Feed it real patient data.
We took the specific Medicare and Medicaid policies relevant to the client's DME portfolio and structured them into a knowledge layer the AI could reason over. Organized by device type, program, conditions, thresholds.
Then we connected it to live patient data from their EMR via HL7 and FHIR. Diagnoses, clinical history, program enrollment, device data. All normalized and governed for PHI compliance.
The engine matches each case against the relevant rules, produces a reimbursement score, and explains which criteria are met, which are borderline, and what's missing. Staff see the answer before the device ships. If a claim gets questioned later, the documentation is already there.
Challenges
Outcomes
Summary
Provided services
Our team
Justin Tannenbaum
Solutions Architect
Lukasz Chmielowski
Lead Engineer
Cesar Bustamante
Engineering
Daniel Bukala
Product Manager
Tech stack
Stop writing off revenue you should be collecting
If reimbursement decisions at your organization depend on tribal knowledge and manual policy review, we should talk.
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