DME & Wound Care AI
AI That DME Providers Can Actually Trust.
Automated coverage determination, wound care documentation, and interoperable workflows — built on deterministic systems that are auditable, reproducible, and ready for production. Patient and provider centric.
TRUSTED BY
THE PROBLEM
The DME Documentation Crisis
DME suppliers and wound care providers are buried in paperwork, denials, and disconnected systems. The cost isn’t just operational — it’s patient care delayed.
Documentation Overload
Clinicians spend 12+ hours per week on wound care paperwork. Each wound requires separate measurements, staging, tissue type, and exudate documentation — often for multiple wounds per patient. Since CMN retirement in 2023, the same data must be extracted from unstructured clinical records.
Denials & Audit Exposure
Advanced wound care claims face 30-35% denial rates nationally. NPWT carries a 17% improper payment rate. SMRC is actively auditing surgical dressings. UPIC audits use statistical extrapolation — a single audit can generate millions in recoupment demands.
Disconnected Systems
70% of DME orders still arrive by fax. EHRs, wound care software, DME supplier systems, and payer portals don't talk to each other. Critical wound data is trapped in unstructured notes, requiring manual extraction at every handoff.
There is a better way.
OUR APPROACH
The Hybrid Architecture
AI extracts. Rules decide. Every determination is auditable.
Unstructured Data
Wound care clinical notes, progress reports, photos, physician orders
LLM Extraction
Extract wound measurements, tissue type, staging, treatment history, HCPCS-relevant details"
Structured Facts
Typed wound assessment objects: L x W x D, % granulation/slough/eschar, exudate type and amount, Bates-Jensen scores
Rules Engine
CMS LCD criteria (L33831 surgical dressings, L33821 NPWT, L33830 support surfaces), modifier logic (A1-A9, KX), quantity limits
Auditable Decision
COVERED: Alginate dressing (A6196) for 3.2 x 2.1cm full-thickness wound with moderate exudate. Evidence: Chart p.4, LCD L33831 criteria 4.2
The Key Insight
The LLM never makes the decision. It extracts structured facts from unstructured clinical text. The rules engine — deterministic, auditable, reproducible — applies CMS LCD criteria to those facts. Every coverage decision traces back to a specific rule, a specific quote from the chart, and a specific page number.
100% Audit Trail
Every decision traceable to source quote and LCD rule
Always Reproducible
Same clinical note = same coverage decision, every time
Full Explainability
Clear reasoning chain from wound data to coverage outcome
CMS LCD Compliant
Rules engine encodes LCD criteria, not LLM guesswork
SERVICES
What We Build
End-to-end AI solutions for DME workflow, wound care documentation, and healthcare interoperability.
DME Coverage Determination
Automated coverage decisions for regulated DME environments.
- Automated LCD compliance checking for DMEPOS
- Wound care documentation extraction (measurements, staging, tissue type)
- NPWT, surgical dressing, and support surface coverage verification
- Prior authorization package assembly
Clinical Rules Engine
Deterministic decision systems built on CMS guidelines.
- 100+ CMS LCD guidelines encoded (L33831, L33821, L33830)
- HCPCS code selection with modifier logic (A1-A9, KX, GA)
- Quantity limit enforcement and dressing-to-wound matching
- Complete audit trail: rule to source quote to page number
Wound Care NLP
AI extraction of structured wound data from clinical notes.
- Extract structured wound data from free-text clinical notes
- Wound measurement, tissue type, exudate, and staging extraction
- Documentation completeness scoring before claim submission
- Bates-Jensen and PUSH score extraction and tracking
Healthcare Interoperability
Connect EHRs, DME suppliers, and payers with structured data.
- Bridge EHRs (Epic, Oracle Health), wound care software, and DME supplier systems
- HL7 FHIR integration with wound care data profiles
- Payer portal connectivity for eligibility, prior auth, and claims
- Eliminate fax-based workflows with structured data exchange
CASE STUDIES
Proven in Production
Real systems solving real problems in DME and wound care.
DME — Wound Care Coverage Determination
Clinical NLP Stack
CHALLENGE
Automate Medicare coverage determination for wound care DME from unstructured clinical documentation while maintaining full compliance with CMS LCD guidelines.
SOLUTION
LLM extracts wound characteristics — measurements, tissue type, staging, exudate, treatment history — from clinical notes. A deterministic rules engine applies CMS LCD criteria including L33831 (surgical dressings), L33821 (NPWT), and L33830 (support surfaces) to the structured facts.
“Every coverage decision traces back to a specific LCD rule, a specific quote from the chart, and a specific page number.”
RESULTS
Healthcare Workflows — DME Order Management
DME Workflow Automation
CHALLENGE
Streamline the DME order-to-delivery workflow — from faxed referral intake through eligibility verification, prior authorization, documentation assembly, and claim submission.
SOLUTION
AI-powered intake digitizes faxed orders, extracts patient and clinical data, and routes through automated eligibility and compliance checks. Rules engine validates LCD coverage criteria before delivery, preventing downstream denials.
“Orders that used to take days of manual processing now flow through in hours — with better compliance than we achieved manually.”
RESULTS
Healthcare Interoperability — Connected Wound Care
Interoperable Wound Care Records
CHALLENGE
Connect wound care assessments across EHRs, specialty wound care systems, DME supplier platforms, and payer portals — eliminating the manual re-entry and fax-based handoffs that delay patient care.
SOLUTION
FHIR-based integration layer extracts structured wound data from clinical systems and delivers it to DME suppliers and payers in the format each system requires. AI fills gaps where structured data isn’t available by extracting from unstructured notes.
“For the first time, wound care data flows from the clinician to the DME supplier to the payer without anyone re-typing it.”
RESULTS
CONTACT
Let's Build Something Together
Ready to implement AI that actually works? Tell us about your project.