Brad Hess
About Brad Hess
Brad Hess serves as the Senior Vice President of Technology at Cloudmed, an R1 company, and has held various leadership roles in technology and software engineering across multiple organizations since 2002.
Current Role at Cloudmed
Brad Hess serves as the Senior Vice President of Technology at Cloudmed, an R1 company, since 2020. In this role, he oversees technology initiatives and strategies that enhance the company's data analytics capabilities. His leadership focuses on optimizing technology solutions to support the organization’s objectives in the healthcare revenue cycle management sector.
Current Role at R1 RCM
In addition to his position at Cloudmed, Brad Hess has been the Vice President of Technology for Data Warehouse, Analytics, and Data Science at R1 RCM since 2022. He manages a team that supports 1,200 data feeds and generates 6,000 reports, ensuring the effective use of data across the organization. His work involves the development of advanced data models and analytics solutions.
Professional Background
Brad Hess has a diverse professional background in technology and software engineering. He co-founded Arc34 in 2016 and has held several leadership roles, including Vice President of Software Engineering at Naveos and Revint Solutions. His experience spans over two decades, including a brief consultancy at Bearing Point and a senior developer role at Mantech.
Education and Expertise
Brad Hess earned a Bachelor of Science degree in Business Administration from the University of Vermont, where he studied from 1994 to 1998. His educational background complements his extensive experience in technology management and software development, particularly in data analytics and centralized data warehousing.
Achievements in Data Analytics
Throughout his career, Brad Hess has led the development of innovative models for data analytics, utilizing large language models (LLM) to enhance revenue generation across various service verticals. His expertise in managing multiple development teams has been instrumental in building centralized data warehouses for enterprise-level analytics.