Health Catalyst on Wednesday announced the launch of its new Healthcare.AI – a range of services and technologies designed with an eye toward helping clients gain ground on their clinical, financial and operational projects.
WHY IT MATTERS
The analytics company says the new suite of augmented intelligence tools is meant to help customers tackle revenue, cost and quality challenges across an array of different use cases: ongoing COVID-19 response, compliance with new price transparency requirements from the Centers for Medicare and Medicaid Services, and healthcare equity efforts, to name just a few.
Health Catalyst notes that, while most hospitals and health systems have become well practiced in analytics, business intelligence and data-driven decision making by now, many are still having challenges with more advanced AI algorithms.
“Self-service predictive models often require tailoring and expert guidance to achieve accurate predictions and results,” the company said in announcing the new Healthcare.AI suite. “Further, expert guidance is required to integrate predictive models into workflows, deploy broadly, and sustain positive change. Lastly, transactional predictive models do not address the many other use cases for AI in healthcare.”
So Health Catalyst says it designed Healthcare.AI to help broaden the use cases its clients could tackle, and the efficacy with which they could do so. Specifically the new product focuses on five levels of healthcare AI analytics:
- Analytics integration
- Choosing and building predictive models
- Optimizing predictive analytics
- Retrospective comparison
- Prescriptive optimization
Level 1, the basic product suite, focuses on one-click AI access and integration into healthcare organizations’ existing BI tools and more than 100 other Health Catalyst applications; it can embed statistical and machine learning techniques into product suite modules, speeding up analytics insights and enabling them to be more accurate and consistent.
Levels 2-5 are more advanced services, the company says – offering guidance to help users deploy their predictive models more effectively and design them to better meet specific use cases, as well as giving guidance on retrospective comparisons, prescriptive optimization and more.
THE LARGER TREND
The ambitions of the Healthcare.AI platform are in line with what the company highlighted as three major AI challenges when Jason Jones, chief analytic and data science officer at Health Catalyst, spoke with Healthcare IT News a year ago: slow or delayed AI results, algorithmic bias and a need for more data science talent to help providers manage complex projects and use cases.
“It is very easy for individuals or organizations to get excited about their first AI project,” said Jones at the time. “It is new, exciting and a bit magical. Out of dreams of doing good or pressure to perform, people would like to believe there is a solution. What is the problem? Building predictive models is very quick and easy.”
Often, however, the reality of the challenge can set in quickly, he said.
“If you fear you are being left behind in the AI race, consider the last time you felt left behind by an infomercial. The claims of success for AI may not be much better founded. Focus on fundamentals, ask challenging questions, realize that AI typically fits into a workflow that requires multiple changes, and plan to monitor and improve over time.”
Soon after that conversation, Health Catalyst turned its focus to helping its customers manage the data demands of the pandemic that was then just emerging. Its Patient & Staff Tracker module offered ability to track where patients who test positive for COVID-19 have been across a health system setting, and which staff members might have interacted with them; its Public Health Surveillance module helped identify unusual patterns of symptoms and tests, to flag them for more timely public health response.
ON THE RECORD
“I came to Health Catalyst to build what I could not buy from within some of the greatest healthcare organizations in the world – tools and services to improve health and healthcare for people at the hospital bedside, in the boardroom, and at their kitchen tables,” said Jones in a statement this week announcing the Healthcare.AI launch.
“In contrast to traditional AI approaches, we designed Healthcare.AI as a new approach to meet the needs of healthcare, enabling better decisions both at the point of care and by those leading system-level change.”