I led development of the Semantic Knowledge Graph, an AI-powered product designed to link clinical concepts across domains such as medications, conditions, and labs. The SKG enabled faster knowledge retrieval, more accurate relationships, and improved data-driven insights for clinicians and downstream applications.

• Users: Clinical terminologists, engineers, data scientists, and downstream product teams.

• Use Cases: Supporting AI-driven summaries, improving chart search, and enabling intelligent agent validation.

• Value I Brought:

  • Designed structured workflows for relationship validation and curation.

  • Defined product requirements that balanced clinical accuracy with engineering scalability.

  • Aligned cross-functional teams around clear deliverables.

I managed initiatives that connected medications to their related conditions using SKG technology. For example, linking antihypertensive medications with hypertension provided valuable insight into treatment pathways.

• Value I Brought:

  • Organized end-to-end workflows for clinical validation.

  • Improved accuracy of medication-condition mappings for downstream AI applications.

  • Helped reduce manual research time for clinical terminologists.

I directed efforts to model and validate condition-to-condition edges, such as linking diabetes to diabetic nephropathy. These relationships supported clinical summaries and predictive modeling.

• Value I Brought:

  • Delivered structured clinical knowledge to support advanced AI use cases.

  • Standardized how relationships were captured and validated across conditions.

I wrote requirements for a tool to enable clinical terminologists to validate and curate SKG relationships in a structured, scalable way.

• Value I Brought:

  • Enabled non-technical users to work directly with AI-generated outputs.

  • Created processes for tagging, approving, and tracking relationship validation.

  • Drove efficiency in knowledge curation across large datasets.

I validated CPT codes against Local Coverage Determinations (LCDs) and National Coverage Determinations (NCDs) to automate prior authorization decisions. This agent reduced friction for providers and payers by surfacing required documentation earlier in the process.

• Value I Brought:

  • Enhanced accuracy of coverage validation.

  • Reduced administrative burden for providers.

  • Connected clinical content with reimbursement processes.

I authored PRDs to automate the validation of lab-to-condition relationships using Elsevier data. For example, connecting elevated HbA1c results with diabetes diagnosis supported both clinical decision support and downstream analytics.

• Value I Brought:

  • Improved reliability of lab-to-condition mappings.

  • Established scalable pipelines for external data integration.

  • Strengthened the clinical foundation of SKG.

I developed a RACI framework for the SKG team to clarify ownership and accountability across clinical terminology, data science, and engineering.

• Value I Brought:

  • Reduced bottlenecks by ensuring clear responsibilities.

  • Improved cross-team collaboration and communication.

  • Enabled more predictable delivery of complex initiatives.