Global Mobility Solutions Provider (unnamed)

For a global mobility solutions provider supporting a network of affiliated clinics, Durable Medical Equipment (DME) insurance claims were processed entirely by hand. Staff at each clinic manually assembled custom claims documents — a time-intensive task averaging 30 minutes per document — before submitting to insurers. This manual workflow introduced frequent documentation errors, inconsistent formatting, and a high rate of claim rejections. In the Property & Casualty context, rejected claims translate directly to delayed reimbursements, strained clinic relationships, and administrative rework that compounds operational costs. The absence of automation created a bottleneck that constrained clinic throughput and undermined the provider's ability to scale its dispatch support platform.

Harbinger, acting as the company's InsurTech partner, designed and deployed an AI-powered automated claims processing solution integrated directly into the client's existing DME dispatch support platform. The core of the solution was a Natural Language Processing (NLP) engine that automated the generation of custom claims documents — extracting relevant patient, equipment, and coverage data and producing structured, insurer-ready outputs without manual intervention. Rather than replacing the dispatch platform, Harbinger embedded the automation within it, ensuring minimal disruption for clinic staff and accelerating adoption across the affiliated network. The integration standardized document formatting and applied validation logic to catch errors before submission, directly targeting the root causes of prior rejection rates.

The AI-powered solution delivered measurable improvements across accuracy, speed, and platform performance:

• 95% accuracy in generating custom claims documents, up from an error-prone manual process
• Document creation time reduced from 30 minutes to seconds — representing a near-total elimination of per-document labor
• 2-second response time per page achieved on the DME dispatch platform, ensuring the automation did not introduce latency for clinic users

Beyond the headline metrics, affiliated clinics gained a consistent, validated document output that reduced insurer rejections. The tight platform integration meant staff transitioned to the automated workflow without a separate tool or retraining burden.

• NLP-based document automation can eliminate the majority of manual effort in claims preparation while simultaneously improving accuracy — the two goals are not in tension.
• Embedding automation directly into an existing operational platform (rather than deploying a standalone tool) significantly reduces adoption friction across distributed clinic networks.
• Consistent, validated document outputs address claim rejection at the source; accuracy improvements upstream reduce costly rework and resubmission cycles downstream.
• Response time targets (here, 2 seconds per page) should be defined as hard requirements during solution design — performance degradation in integrated platforms can erode adoption gains quickly.
• DME claims workflows with high document variability are well-suited to NLP automation, where template rigidity would fail but learned extraction handles edge cases.