The Madison, Wisconsin-based company’s platform automates complex data processes for healthcare payers. By leveraging artificial intelligence and machine learning, the platform digests and transforms provider roster data through formatting and validation. It then loads the data directly into the claims system.
“We move dollars from the costly, administrative processes in healthcare to places in the system where we can keep people healthy and well,” said Meghan Gaffney, founder and CEO of veda, in an email. “Automation allows for redistribution of resources and helps health plans move more money to things that matter for people.”
The company’s platform can also help payers comply with the regulatory requirements of the No Surprises Act. The law restricts surprise billing, which is when patients are charged for out-of-network care, often unexpectedly. The law requires payers to process all provider directory updates in under 48 hours starting Jan. 1, 2022, which the veda platform can help with.
The company plans to use the newly raised funds on product innovation as well as growing its customer service and go-to-market teams.
“We think that we’ve really just dipped our toes into the problems our technology can solve,” Gaffney said. “Our platform provides efficiencies across a spectrum of healthcare companies, and we needed funding to help create those additional efficiencies and venture into other health categories.”
Since its Series A funding round in January 2020, veda has raised $53 million.
“Veda’s technology will fundamentally change how the healthcare system operates by automating processes, increasing accuracy, and reducing costs that have existed for decades,” said Andrew Adams, managing partner and co-founder of venture growth-equity fund Oak HC/FT, in a news release. Adams will join veda’s board of directors.
Veda’s offerings aren’t new in the industry, with companies like SDLC Partners and Appian providing similar solutions. But it sets itself apart by offering a platform that doesn’t require skilled IT resources, Gaffney said.
“Our tools are generalizable so that the users need no technical skills or training —they provide information to the system but it does not rely on user inputs for data science or automation logic,” she said.
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