Mednition raises $10M in Series A funding round led by Concord Health Partners

Proceeds from the financing will be used to expand the company’s customer care team as Mednition further scales commercial deployment of its machine learning platform.
By Jeff Lagasse
02:25 pm

Mednition, makers of machine learning-powered solutions for healthcare, announced Wednesday that the company has raised $10 million in a Series A round of funding led by healthcare private equity firm Concord Health Partners. Concord tapped its American Hospital Association Innovation Fund for the financing.


Burlingame, California-based Mednition was founded in 2014 to help clinicians improve care delivery. Starting initially in the emergency department, Mednition is providing emergency nurses with KATE, a machine learning clinical decision support solution now in use at Adventist Health White Memorial in Los Angeles. The company is funded by a select group of private investors and major healthcare financial institutions, including Concord Health Partners.

“We have first-hand evidence that machine learning is improving healthcare delivery and saving lives,” Mara Bryant, operations executive at Adventist Health White Memorial, said in a statement. “Using KATE, we have documented increased clinical accuracy, patient safety and operational efficiency, while using the same processes and existing EHR systems. Our nurses see KATE as a critical new machine learning-powered tool, like an expert advisor who increases their accuracy, ensures patients received the right care at the right time, and helps calm the chaos frequently associated with triage.”


Proceeds from the financing will be used to expand the company’s customer care team as Mednition further scales commercial deployment of KATE. AHWM partnered with Mednition in late 2016 to research machine learning in support of clinical decision making. 

KATE was deployed in December 2018 when the study results showed the potential to improve triage accuracy by 26.9% for every patient presenting at their emergency department, and up to 93.2% for high acuity patients. This study and its findings are currently in peer-review and can be accessed on the Mednition website.

KATE and will be debuting publicly for the first time at next week’s Emergency Nursing 2019an industry conference dedicated to emergency nursing. The conference is being held in Austin at the Austin Convention Center from September 29 to October 2.


Machine learning is increasingly being used to help power clinical decision support tools. Just this spring, artificial-intelligence-enabled monitoring and decision support platform Biofourmis announced a $35 million Series B raise. Using a proprietary wearable, a patient-facing app and AI technology, the system is able to create digital biomarkers “reflecting the health status” of a patient, helping to monitor patients as they take different medications.

Even retain giant Amazon is getting in on the act, with machine learning tools such as Amazon Comprehend Medical, a HIPAA-eligible service that’s able to pull out medically-relevant information such as patient diagnoses, symptoms, medical test details, treatments and dosages, while simultaneously highlighting any protected health information.


“We were founded on a passion to help clinicians improve healthcare delivery significantly and save lives,” said Steven Reilly, CEO and cofounder of Mednition, in a statement. “We’re transforming how clinical decisions are made in real time. Having Concord Health Partners demonstrate their confidence in us, by leading this financing, validates our company’s strategy, team and technology.”

“Concord is focused on identifying and supporting healthcare companies with innovative solutions that lower costs, improve safety, and expand access to quality healthcare,” said James Olsen, founder and managing partner of Concord Health Partners, in a statement. “Mednition hits our criteria and is already using machine learning to advance healthcare where it’s most critical, at the point of care with emergency department clinicians and patients.”



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