
Health Care Technology
Daffodil Health Launches To Uber-Automate Healthcare Pricing And Bring Down Admin Spend

The company closed a $4.6 million seed investment led by Maverick Ventures to build AI-powered healthcare pricing and admin platform
SAN FRANCISCO--(BUSINESS WIRE)--Daffodil Health, provider of healthcare pricing and administration AI-powered solutions, today announced it raised $4.6 million in a seed funding round led by Maverick Ventures, with participation from Epic Ventures.
The funding announcement comes at a time when more than $1T is spent on healthcare admin, much of it wasted and with limited to no benefit to patients. Technologies like AI and large language models make it possible to uber-automate healthcare processes and make middlemen redundant.
The company was started by a group of healthcare and technology experts with over 100 years of combined healthcare and software experience at companies such as MultiPlan, Google, and McKinsey. They believe that change in how healthcare navigates pricing and billing isn’t just possible, but a dire necessity.
“It is not lost on us that truly reducing healthcare admin spend is difficult. We are also cognizant that numerous well-funded companies and entrepreneurs have had limited success to date in this realm,” said Navin Nagiah, CEO of Daffodil Health. “We also fervently believe that what we are doing, needs to be done. Our first application, a SaaS-based and AI-enabled OON/RBP repricer is an important first step in our mission.”
The first problem the company has targeted is the complete automation and AI enablement of out-of-network (OON) and Reference Based Pricing (RBP) processes for payors and Third Party Administrators (TPAs). Daffodil is specifically focused on the automation and AI enablement of how to reprice, benchmark and defend a claim that is outside of a plan’s network.
Daffodil’s SaaS-based, AI-powered solution allows for:
- Real-time repricing of claims based on the payor or TPA’s preferred pricing methodology
- Easy configuration of complex pricing rules
- Automatic benchmarking of the repriced claims and
- Generation of detailed and defensible pricing rationale powered by AI/LLM models
The company is delivering the above as a cloud-native SaaS solution that is super easy to use and will result in both improved productivity and dramatically lower costs. With early and deep collaboration with digitally forward-looking and innovative payors and TPAs, the application is expected to go live with multiple implementations in the next few months.
“The intricate challenges of out-of-network repricing demand innovative solutions that marry simplicity with efficiency,” said Andrew Boyd, executive vice president and general manager, healthcare solutions at Personify Health, a provider of health plan administration and cost containment solutions. “There is an opportunity for an end-to-end AI driven approach to streamline these processes and achieve remarkable cost savings and enhanced operational efficiency for TPAs and payors.”
"The out-of-network billing dilemma has hit a fever pitch in recent years. From payors to TPAs and employers, it's perceived as an oligopoly with no winners," said Prateesh Maheshwari, Managing Director at Maverick Ventures. "Daffodil is boldly addressing this issue through cutting-edge AI, transforming claims adjudication and slashing administrative costs. With new price transparency laws shaking up the game, it's prime time to revolutionize claims processing and communication with AI."
For more information, please visit: www.daffodilhealth.co
About Daffodil Health
Daffodil was founded by a group of passionate healthcare and technology experts with the lofty mission of using technology, data and AI to automate healthcare pricing processes and reduce administrative spend. The company’s first application, an AI/LLM-enabled SaaS Repricer, was launched in 2024. The application is focused on automating the out-of-network (OON) and Reference Based Pricing (RBP) repricing processes end-to-end.
ContactsPR Contact
support@daffodilhealth.co
First published on Thu, May 16, 2024
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