I managed Precision Normalize, a core product designed to map and normalize clinical terms across disparate data sources using NLP. This product ensured clinical data was accurate, standardized, and interoperable for downstream use.

• Users: Health systems, payers, researchers, and product teams.

• Use Cases: Mapping free-text clinical documentation to standard terminologies, improving interoperability across EHRs, and supporting downstream analytics.

• Value I Brought:

  • Authored requirements and prioritized backlog items for NLP accuracy improvements.

  • Oversaw professional services projects for large healthcare organizations, ensuring customized deployment.

  • Strengthened product adoption and retention by aligning with client workflows and compliance needs.

I guided the development of the NLP Workbench, a user interface that provided customers visibility into NLP outputs, annotation workflows, and normalization results.

• Value I Brought:

  • Delivered usability enhancements for clinicians and technical end users.

  • Partnered with UX and engineering to translate customer feedback into design changes.

  • Enabled more efficient NLP customization for customers.

I led initiatives to research, evaluate, and deploy large language models as part of IMO’s AI platform. These LLMs powered explainable clinical concept mapping and supported core product services such as Normalize and Knowledge Graph.

• Value I Brought:

  • Directed evaluation of open-source and proprietary models to reduce vendor lock-in.

  • Defined scoring and explainability frameworks to ensure clinical reliability.

  • Integrated LLM agents across multiple clinical domains to strengthen accuracy and performance.

I contributed to the development of Ambient AI, enabling real-time transcription and clinical documentation assistance.

• Value I Brought:

  • Partnered with engineering and commercialization teams to define features and market positioning.

  • Supported adoption by emphasizing reduced clinician documentation burden.

  • Helped establish IMO’s role in ambient technology and generative AI adoption.

I managed initiatives within professional services to deliver custom NLP development for major healthcare clients, including children’s hospitals, pharmaceutical data providers, and health IT vendors.

• Value I Brought:

  • Translated client requirements into structured backlog items and delivery milestones.

  • Oversaw retention-focused projects that improved adoption of IMO products.

  • Strengthened client relationships by delivering measurable improvements in NLP performance.

I directed development to ensure IMO’s NLP platform achieved parity with industry-leading tools. This included adding pre-annotation capabilities, optimizing pipelines, and introducing automation in data quality workflows.

• Value I Brought:

  • Delivered new features for annotation efficiency and accuracy.

  • Reduced cost and improved compute performance for large-scale NLP workflows.

  • Strengthened IMO’s competitive positioning in the healthcare AI market.

I managed the development of LLM agents that supported term normalization and interoperability across IMO’s products.

• Domains: Problem, Procedure, Medication, and Lab.

• Value I Brought:

  • Designed explainable, domain-specific LLM agents that mapped terms to SNOMED CT, CPT, RxNorm, and LOINC.

  • Defined processes for fine-tuning and validation across multiple domains.

  • Aligned LLM development with IMO’s overall AI strategy.

I partnered with engineering and data science teams to expand IMO’s knowledge graph, ensuring accurate, scalable representation of clinical concepts.

• Value I Brought:

  • Defined curation workflows and ontology structures.

  • Improved content generation and maintenance efficiency.

  • Supported downstream AI applications with high-quality knowledge relationships.

I spearheaded initiatives to prototype and validate explainable LLM agents for ICD-10-CM, CPT, RxNorm, and LOINC domains. Each prototype supported accurate code matching and was integrated into a broader LLM service.

• Value I Brought:

  • Authored PRDs that defined explainability requirements and deployment workflows.

  • Partnered with UX to ensure agents could be integrated into front-end tools.

  • Ensured final deployments were clinically valid, scalable, and ready for production adoption.