Andrew Eye, ClosedLoop.ai’s CEO and Co-Founder, recently sat down for an interview with the Ideas to Invoices podcast to talk about his latest venture. Eye previously founded and sold two other technology companies.
The company is in the midst of a hiring push following its $34 million Series B round led by Telstra Ventures. Currently 35-strong, ClosedLoop wants to grow its team to 60 by year’s end and to 100 by the end of 2022 in roles across engineering, data science, customer success, support and more.
In the past week, five Austin companies reported roughly $249.4 million in funding deals... Austin-based health data platform developer ClosedLoop.ai reported Aug. 25 raising about $33.9 million from 14 investors, according to a regulatory filing.
ClosedLoop.ai, a healthcare data science platform, has raised $34M in a Series B funding round. This Series B brings ClosedLoop.ai's total funding to $49.7M. Here are the top-line bullets you need to know...
Now, ClosedLoop.ai has $34 million in fresh funding to "pursue more problems" plaguing the $4 trillion industry, ClosedLoop co-founder and CEO Andrew Eye told Fierce Healthcare during an interview at the Healthcare Information and Management Systems Society (HIMSS) global conference in Las Vegas last week.
ClosedLoop.ai, an Austin-based healthcare startup, raised new funding after being picked by CMS as the winner of its AI Health Outcomes Challenge. The company’s secret? Working with health systems to build algorithms that are explainable.
ClosedLoop.ai said it raised $34 million Series B financing for its healthcare-specific data science platform, bringing its total funding to $48 million. The company’s software combines machine learning with a library of healthcare-specific training features and model templates.
ClosedLoop and these other newer additions underscore the breadth of opportunity in the sector and specifically represent a new frontier for leveraging previously inaccessible data sets. The teams at these companies are attacking different problems, but share a broader thesis that better data, when properly leveraged, will lead to transformative health insights and better patient outcomes.
Back when healthcare data science platform ClosedLoop.ai was in its early days, co-founder and CEO Andrew Eye underwent a three-week “odyssey” of testing and waiting for diagnostic results concerning his daughter’s inflamed liver. By the time they finally got the diagnosis of autoimmune hepatitis, the eight-year-old was two weeks away from getting a liver transplant when the appropriate treatment was an inexpensive dose of the prescription drug prednisone.
ClosedLoop.ai, healthcare’s data science platform, today announced a partnership with Southwestern Health Resources (SWHR), a national leader in population health management. ClosedLoop’s explainable artificial intelligence (AI) helps SWHR enhance its analytics capabilities to predict and reduce unplanned hospitalizations, and better identify and understand the unique needs of patients, so it can continue to set the industry standard for population health management.
ClosedLoop.ai, healthcare’s data science platform, today announced a $34 million Series B financing. The investment round, led by Telstra Ventures with participation from Breyer Capital, Greycroft Ventures, .406 Ventures, and Healthfirst, positions ClosedLoop to extend its lead in delivering artificial intelligence (AI) solutions that tackle some of healthcare’s biggest challenges.
ClosedLoop.ai, healthcare’s data science platform, today announced a new relationship with Palm Beach Accountable Care Organization (PBACO), one of the nation’s leading and most successful physician-led ACOs in the Medicare Shared Savings Program (MSSP).
ClosedLoop makes it easy and affordable for healthcare organizations to use data science to improve outcomes and reduce costs by combining an intuitive end-to-end machine learning platform for data scientists with a catalog of healthcare specific predictive models and features.
KLAS examines the recent AI purchase decisions of 47 organizations to determine which vendors are being considered and chosen, which are being replaced, and why provider and payer organizations choose the AI solutions they do (technology? service? healthcare expertise?).