Nym Health Raises $25 Million for Medical Coding Software

Nym Health, a developer of AI-powered autonomous medical coding software, announced a $25 million in new funding led by Addition.

Existing investors GV, Dynamic Loop Capital, Tiger Global, Bessemer Venture Partners, Lightspeed, and angel investors Zach Weinberg and Nat Turner also participated in the funding round.

Following Nym’s October 2020 Series A, this latest round brings the company’s total funding to $47.5 million.

The company plans to use the new funds to scale product development and accelerate the rapid adoption of its automated medical coding platform in emergency departments. Additionally, the funding will support expanding its footprint in urgent care centers.

According to the company, its software is deployed in more than 40 hospitals across the United States, including Geisinger and several other academic medical centers. The Nym platform modernizes revenue cycle management using direct-to-billing, fully autonomous medical coding that reduces insurance denials and operational expenses, accelerates payment cycles, and maximizes audit readiness for healthcare providers.

“COVID has shown provider organizations that solely relying on people to perform business critical work is unsustainable. We saw demand for our platform surge amid the global pandemic, when hospitals and urgent care centers across the US found they needed to work faster, smarter and more efficiently under the most challenging of circumstances,” said Nym Health CEO Amihai Neiderman.

Aaron Schildkrout of Addition, who previously served as Head of Data & Growth at Uber, added: “Antiquated, manual billing processes typically result in coding-related denials from insurers that add up to billions in lost revenue annually for healthcare providers, but Nym’s automated, auditable medical coding platform is a game-changer. Nym is poised to transform revenue cycle management by significantly improving the speed and precision of billing, reducing denial rates and traditional staffing costs, and, in turn, enabling human coders to focus their attention on more complex cases and follow-up queries.”