CodaMetrix Closes $55M Series A



CodaMetrix, the leading AI technology platform that is transforming healthcare revenue cycle management, announced today the completion of a $55 million Series A round led by SignalFire. The round was also attended by Frist Cressey Ventures (FCV), Martin Ventures, Yale Medicine, the University of Colorado Healthcare Innovation Fund, and the Mass General Brigham physician organizations. Chris Scoggins, a SignalFire Partner, will join the CodaMetrix Board of Directors.

The capital injection will hasten go-to-market with major provider organizations and health systems, as U.S. healthcare grapples with high coding costs, increasing complexity, and ongoing skilled labor scarcity, emphasizing the critical need for automation to address chronic inefficiencies that continue to waste 25-30% of every dollar spent in healthcare.

Chris Scoggins highlights the factors that led to partnering with CodaMetrix, saying, “CodaMetrix’s innovations in AI technologies and the experienced team of healthtech executives are a couple of key factors that went into our decision to partner with the company to systematically improve the way our health system accounts for patient care in both billing and clinical cycles.” He also mentions that the platform was not possible a few years ago, but now, with advances in AI and cloud computing, CodaMetrix has built a platform that works well for physicians, financial administrators, and health systems.

CodaMetrix (CMX) is a SaaS technology company providing an AI-powered platform to facilitate healthcare revenue cycle management and medical coding. The company’s cutting-edge platform combines machine learning (ML), deep learning, and natural language processing (NLP) to translate clinical notes automatically, accurately, and autonomously into billing and diagnostic codes that satisfy coding requirements while reducing human coding workload. CodaMetrix’s AI continuously learns from and acts upon the clinical evidence in electronic health records (EHRs). That knowledge is applied to improve the efficiency and quality of medical coding and to enable providers to code less and care more.

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