Danny Burrow

Led development of ambient intelligence and conversational AI platforms integrated with Oracle Health systems for automated patient triage, scheduling, post-visit engagement, and virtual care coordination. Designed Agentic AI and Large Action Model (LAM) based healthcare automation for patient journey orchestration and intelligent follow-up workflows. Built ambient clinical documentation using LLMs to summarize physician-patient conversations within EHR workflows, reducing clinician documentation time by 25-30%. Implemented AI-powered Revenue Cycle Management automation for prior authorization, denials, and claims adjudication. Architected cloud-native governance and HIPAA/CCPA-compliant security across Azure and AWS for 100TB+ datasets. Led DataOps and interoperability pipelines processing millions of FHIR/HL7/DICOM records daily. Built predictive analytics and real-time healthcare intelligence to forecast ER demand and bed utilization. Mentored 5+ AI, backend, and data engineers.

Built scalable cloud-native backend and analytics platforms across Azure and AWS supporting AI model processing and enterprise clinical workloads. Led EHR interoperability and data standardization via scalable FHIR/HL7 pipelines, improving real-time analytics and readmission risk prediction. Engineered real-time COVID-19 tracking and healthcare intelligence pipelines by integrating EHR, laboratory, and mobility data. Developed large-scale genomic and phenotypic data platforms to support AI-driven precision medicine and personalized oncology. Built computer vision pipelines for COVID-19 detection on multinational chest CT datasets. Designed AI-powered clinical trial recruitment using NLP and EHR mining. Implemented virtual clinical assistant and intelligent medical scribing platforms, enabling physicians to dictate notes while extracting structured data. Mentored teams and ensured HIPAA-compliant AI deployment.

Built scalable AI-powered backend pipelines supporting Alexa services and AWS AI capabilities. Developed NLP, speech recognition, and conversational components improving voice accuracy, contextual understanding, and real-time latency at scale. Architected cloud-native AI systems on AWS using distributed microservices and real-time data processing. Contributed to recommendation and personalization platforms leveraging large-scale customer data and ML-driven ranking. Built and optimized ML data pipelines for training and inference across AWS infrastructure. Collaborated with AI research, infra, and product teams to deliver production-grade features across voice automation and cloud-based AI services.