A recent OptumIQ annual survey of major healthcare organizations on AI in Healthcare shows an average of $32.4M investment per organization over the next 5 years. In planning an AI strategy, It would help to understand how AI may be added into the current IT mix. AI may be included in an existing application or integrated with applications in a workflow. Or in the lesser-known, process-centric approach, AI may encapsulate the workflow, which arguably would take us to the next frontier. EHR vendors, consistently blamed for interfering with the patient-provider relationship for their applications' subpar UI/UX, strive to innovate by adding AI in their applications.

The artificial intelligence (AI) in health care market is set to top $34 billion by 2025, according to some estimates -- and it's no real wonder why. One startup that's successfully maintained pole position is Health Catalyst, a Salt Lake City, Utah-based health care big data company founded in 2009 by Steven Barlow and Thomas Burton. It aims to drive clinical and operational performance improvements in state and regional health plan providers, physician groups, and extended care facilities through its suite of analytics apps. And it's raising capital to help further progress toward that goal. Health Catalyst today announced that it has secured $100 million in series F equity and debt financing led by health care investment firm OrbiMed, with participation from existing partners Sequoia Capital, Norwest Venture Partners, Sands Capital Ventures, UPMC Enterprises, and Kaiser Permanente Ventures.

It is safe to say that there are too many manual processes in medicine. When in training, I write lab scores, diagnoses, and other graphic notes on paper. I always know this is an area where technology can help improve my workflow and hope it will also improve patient care. Since then, progress in electronic medical records has been extraordinary, but the information they provide is not much better than the old paper charts they replaced. If technology wants to improve care in the future, then the electronic information provided to doctors needs to be enhanced by analytical power and machine learning.

Bon Secours Charity Hospital, a three-hospital health system that is part of Westchester Center Health Network, also known as WMCHealth, was using a risk scoring algorithm in its electronic health record that was not very accurate. As a result, WMCHealth missed some high-risk patients and classified other patients as high-risk who were not. In addition, the automated daily report sent to case managers included only patients who had primary care doctors. The case managers also wasted a lot of effort digging through charts to decide which patients to prioritize and which interventions to select. That reduced the amount of time they had to spend with patients.

Health Catalyst believes machine learning is the life-saving technology that will transform healthcare. Machine learning challenges the traditional, reactive approach to healthcare. In fact, it's the exact opposite: predictive, proactive, and preventative--life-saving qualities that make it a critically essential capability in every health system. Health Catalyst is on a mission to help health systems save lives by making machine learning routine, actionable, and pervasive through catalyst.ai Some may ask whether machine learning is just a technology fad or whether it will provide true value in healthcare.

Artificial intelligence: Savvy hospitals are deploying AI and its technological brethren cognitive computing and machine learning in specific use cases at this point – while industry luminaries are predicting that their advancement will soon start happening more quickly than previously anticipated. "I've never in my career seen the acceleration of technology as fast as what we've witnessed in machine learning during the last two years," said Dale Sanders, executive vice president at Health Catalyst. Sanders, it's worth noting, has a U.S. Air Force background working on stacked neural networks and fuzzy logic, which used to be called deep learning, as well as serving as the CIO of both Northwestern University and national health system of the Cayman Islands. "The rate of improvement happening in machine learning," Sanders added, "is way beyond what Moore's Law is to chips." Hospitals already deploying AI As the next generation of both patients and caregivers – including clinicians, doctors, nurses, specialists, even executives and administrators – starts taking a foothold in the healthcare workforce, hospitals looking for a first-mover advantage already know that AI is on the verge of becoming a critical component across the entire organization, and not just IT. "AI and machine learning are exciting opportunities for us to accelerate," Carolinas HealthCare Chief Information and Analytics Officer Craig Richardville said.

One of the more interesting panels at last week's Health Datapalooza featured four speakers involved in the application of artificial intelligence to healthcare, including the creation of predictive models. In areas involving massive amounts of information in the diagnostic and genomic space, machine learning is already in use today, and the FDA is starting to approve applications of deep learning. For instance, a company called Arterys recently won FDA approval for its Cardio DL application, which uses deep learning to automate time-consuming analyses and tasks that are performed manually by clinicians today. Although they each come at it from a different angle based on their company's focus, there were several overarching themes the Datapalooza panelists tackled about the application of algorithms in healthcare, including the importance of transparency to getting clinician engagement. Getting buy-in from clinicians is a huge challenge, said Eric Just, a senior vice president for product development at Health Catalyst, which builds analytics and decision support tools for its health system customers.

Machine learning is a part of everyday life for most Americans, from navigation apps to Amazon's omniscient purchase recommendations. But in healthcare the use of machine learning has so far been limited to niche science projects in large and academic health systems – those able to afford the highly skilled data scientists and dedicated teams required to turn their data into meaningful performance improvements. Health Catalyst is on a mission to change that by embedding the value of machine learning throughout healthcare. Last month, the company launched healthcare.ai to help make machine learning routine, pervasive and actionable for healthcare organizations of all sizes. The collaborative, open source repository of machine learning tools and expertise including topical blog content and weekly live hands-on machine learning educational broadcasts, makes it easy to deploy machine learning in any environment.

Artificial intelligence: Savvy hospitals are deploying AI and its technological brethren cognitive computing and machine learning in specific use cases at this point – while industry luminaries are predicting that their advancement will soon start happening more quickly than previously anticipated. "I've never in my career seen the acceleration of technology as fast as what we've witnessed in machine learning during the last two years," said Dale Sanders, executive vice president at Health Catalyst. Sanders, it's worth noting, has a U.S. Air Force background working on stacked neural networks and fuzzy logic, which used to be called deep learning, as well as serving as the CIO of both Northwestern University and national health system of the Cayman Islands. "The rate of improvement happening in machine learning," Sanders added, "is way beyond what Moore's Law is to chips." Hospitals already deploying AI As the next generation of both patients and caregivers – including clinicians, doctors, nurses, specialists, even executives and administrators – starts taking a foothold in the healthcare workforce, hospitals looking for a first-mover advantage already know that AI is on the verge of becoming a critical component across the entire organization, and not just IT. "AI and machine learning are exciting opportunities for us to accelerate," Carolinas HealthCare Chief Information and Analytics Officer Craig Richardville said.

The winds of healthcare tech are changing -- at least when it comes to trends. It's clear from the recent HIMSS Annual Conference & Exhibition that mHealth is becoming the norm, while machine learning and AI is creeping its way in as a dominant trend in the industry. It's not just at conferences though; pay attention to news and press releases, and you'll see talk of machine learning and how it's being used to do everything from predicting illness to using healthcare analytics to reduce hospital readmissions. One of the most recent developments comes from a company that's set out to make artificial intelligence "routine, pervasive and actionable" for the entire spectrum of healthcare organizations. In mid-February, healthcare data and analytics company Health Catalyst announced its plans to embed the benefits of next-generation healthcare analytics throughout the healthcare experience.