Medial EarlySign, a machine learning company focused on improving patient management and non-communicable disease management, announced at HIMSS18 a $30 million Series B round of financing, bringing the company’s total funding to $50 million.

The round was led by aMoonFund, Marius Nacht’s investment arm in the life science sector. Nacht is Check Point Software Technologies’ co-founder. The deal also included the participation of Horizons Ventures and Nir Kalkstein, founder of high-speed trading firm Final and co-founder of Medial EarlySign.

Medial EarlySign, in Booth 8104 at HIMSS18, uses AI to leverage existing blood test results and EHR data to provide precise insights to healthcare organizations as they determine the best approach to improving patients’ health.

“There is an incredible volume of meaningful routine healthcare data,” said Ori Geva, co-founder and CEO of Medial EarlySign. “Yet, while these data are electronically stored and available, they are still underutilized, and providers can glean much more actionable intelligence from it. Our expertise in these data and predictive health technology allows us to provide valuable insights and create improved actionable opportunities for early intervention, improved decision making, more effective care management, and physician and patient empowerment.”

This new funding will allow the company to further broaden its suite of systems and expand clinical research and global implementations of its clinically supported technology, Geva added. The insights that can be enabled could bring added value to almost every interaction with the patient, with the potential to positively impact millions of lives, he said.

On another AI front at HIMSS18, Orion Health unveiled its machine learning service, Amadeus Intelligence, designed to help the healthcare industry reduce operating costs and improve patient care. Led by new research in machine learning, Orion Health is exploring meaningful ways to minimize waste in healthcare and help clinicians make more accurate decisions at the point of care, the company said.

According to the Center for Medicare and Medicaid Services, more than a third of the United States’ $3 trillion healthcare expenditure is wasted on unnecessary services and excess administration. With an aging population and a growing number of people with chronic illnesses, the issue is set to worsen.

“More than $1 trillion in the U.S. is wasted each year on costly administration and avoidable hospital readmissions,” said Ian McCrae, CEO of Orion Health. “Orion Health’s Amadeus Intelligence will use machine learning models initially to predict patient costs and readmission risks, analyze clinical and financial outliers and enable accurate diagnosis coding and quality metric reporting to improve decision making at the point of care and target resources that will result in significant cost savings.”

Executives have yet to see the true impact of machine learning on healthcare. The last decade has been focused on integrating IT systems and capturing massive amounts of information about patients and their environments.

“The next decade will be to connect all that data and use machine learning for daily healthcare decisions, driving improved care, operational efficiencies and cost-effectiveness,” said McCrae, who is at Booths 5454 and 11955 at HIMSS18.

Elsewhere at HIMSS18, Real Time Medical has debuted its AICloudQA platform. The company offers a new, secure, expandable healthcare quality assurance system that combines Google Cloud Platform infrastructure, Client Outlook’s eUnity viewer, and Real Time Medical’s Intelligent Peer Review system. AICloudQA combines AI-assisted peer learning and AI-assisted workload balancing to increase clinical efficacy and improve acute care performance. It currently is available for radiology and pathology.

To deliver on the promise of AI-assisted peer learning, Real Time Medical is creating large-scale, anonymized, peer learning networks. It is making access to such networks designed to be easy to use with its Google Cloud Platform offering that combines Real Time Medical’s advanced peer learning system with the latest from Client Outlook’s eUnity viewer. Healthcare organizations of any size can access the best in peer learning through a cost-efficient, easy to use platform, Real Time Medical said.

“We understand that clients are seeking to implement solutions capable of both prospective and retrospective peer review while also providing the advanced learning opportunities for physicians and quality improvement for patients that artificial intelligence enables,” said Ian Maynard, CEO of Real Time Medical. “With Google Cloud Platform regions located in the USA, Canada, Brazil, UK, Germany, Finland, Netherlands, India, Singapore and Australia, the solution will enable large-scale peer learning networks while providing flexibility to deploy resources in specific locations or address multi-regional and global needs.”

The AICloudQA system already makes it possible to combine several functions that allows the organization to derive the greatest value from its investment in peer learning and quality improvement, and the system is applicable across medical disciplines, said Manohar Shroff, MD, professor of radiology at the University of Toronto, radiologist-in-chief at the Hospital for Sick Children, and an AICloudQA user.

Twitter: @SiwickiHealthIT
Email the writer: bill.siwicki@himssmedia.com