MACHINE LEARNING
Breast Cancer Detection
A Machine Learning Product for Breast Cancer Detection using Ultrasound - ASP.NET C# web interface for radiologists and technologists to present diagnosis data from the AI engine. The engine analyzes breast lesions from ultrasound DICOM files and provides a “second opinion” to a radiologist with a BI-RADS rating of 2-5 to detect malignancy. The system went from ‘concept’ to being used in multiple large radiology clinics in the US.
DUE DILIGENCE
Telemed Assessment
A 30-day review of the technologies and business strategies for a small Telemedicine vendor. The resulting work product was a business and technology assessment of the vendor and their product along with a recommendation to not buy.
ELECTRONIC CONSENT & QUALITY
HAPI FHIR
Allows physician groups to submit quality measures to a CMS API for reimbursement.
Tech was React JS, Java microservices, GraphQL, Docker/Kubernetes and HAPI FHIR Clinical Data Repository. Data was ingested using a variety of industry-standard formats (CCDA, FHIR, HL7v2, etc.). Analysis and scores were presented via a dashboard where users could review, adjust and resubmit corrections.
