Clinical AI for Payment Accuracy & UM Automation

Architected the expansion of an early-stage ML model into a proprietary clinical intelligence platform enabling enterprise-scale automation across medical necessity review, payment integrity, UM, case management and Pharmacy UM.
• Unified claims, codes, clinical notes, medical history, patient/provider data, and historical determinations for deep learning on 360° real-world evidence.
• Developed proprietary computational linguistics models composing a mult-layered NLP pipeline to extract and structure unstructured clinical evidence from EHR notes, enabling high-confidence, explainable Clinical AI - automating and accelerating evidence-based decisions, revenue cycle and payer operations.
• Integrated with provider EHR workflows and national exchange infrastructure completely eliminating documentation chase and manual burden at the point of care.
• Delivered nearly 90% faster TAT, 60% lower clinical review costs, fewer appeals/P2Ps, and a 33% improvement in provider satisfaction (CAHPS).
• Scaled confidentially across a national production footprint, reaching appx. 40% of U.S. patients while preserving strategic IP and payer competitive advantage.
• Expanded into a foundational enterprise AI architecture for payer operations beyond clinical review by making system components available as microservices to support the payer enterprise including pharmacy, specialty pharmacy, care management, risk adjustment, and population health.

   

Enterprise AI Architecture & Transformation

• Conceived and architected an AI-native pharmacovigilance platform for a top 5 global pharmaceutical company -- years before widespread AI adoption in a closed RFP. Selected as an external AI technology vendor, the design transformed a labor-intensive, fragmented and labor-intensive AERS database into a highly automated, intelligence-driven patient-safety platform.

• Assembled and led data science and engineering teams to rapidly build a working end-to-end prototype in less than 3 months, featuring multimodal ingestion, clinical language intelligence, knowledge representation, advanced NLP, human-in-the-loop validation, model governance, signal detection, and regulatory workflows. This was in 2018, anticipating today's leading pharmaceutical AI capabilities.

• Owned the complete engagement for the technology vendor - from solution design to proposal, pitch and P&L. Sole responsibility for the commercial business case, pricing, staffing, resource requirements, productization, financial projections and, ultimately, the successful delivery of the platform to production.

• The architecture and capability foundation delivered directly informed the pharma giant's long-term roadmap and AI-enabled patient safety strategy.

   

Innovation

Venture Strategy & GTM Commercialization

• Trusted advisor to healthcare C-suite executives, PE/VC investors, founders, and technology leaders translating emerging AI capabilities into market-ready healthcare platforms.
• Convert executive priorities, clinical workflow needs, budget dynamics, and enterprise buying behavior into market insights, product positioning, investment theses, and GTM strategy.
• Build TAM, ROI, market-sizing, and customer-value models to quantify provider impact, investment potential, and one-, three-, and five-year growth scenarios.
• Advises on technical capabilities in product design, product-market fit, founder-market fit, category maturity, competitive positioning, technical diligence, and scalable commercialization pathways.
• Designs executive market-validation tests where innovators and investors ga solutions directly with health system decision-makers.
• Productize and de-risk healthcare AI platforms across EMRs, medical devices, diagnostics, clinical workflow, care coordination, research operations, and clinical trial activation.
• Craft business models, AI engineering operating models, financial planning, pitch narratives, and customer/investor pipelines for emerging healthcare AI ventures and spin-outs.