A new report from KLAS Research and the College of Healthcare Information Management Executives (CHIME) describes the experience of healthcare organizations that are early adopters of artificial intelligence (AI) and are experiencing increasing success in clinical, financial and operational areas.
The research is based on interviews with 57 organizations reporting KLAS-validated use cases across 11 categories. KLAS validated a total of more than 90 use cases in the following segments: population health, clinical decision support, clinical research, patient engagement, clinical education, value-based reimbursement, revenue cycle, financial health, waste/cost/fraud avoidance, employee experience and bed/patient and staffing management.
Measured in the report are purpose-built AI vendors, who are focused on analytics and AI; and analytics platform vendors with AI infrastructure, whose technology is often used as the embedded AI foundation for other vendor products that are industry specific. Not measured, however, are EHR vendors with AI capabilities and health IT application vendors with AI capabilities.
The report’s researchers pointed out, “While it is exciting to see AI being adopted across a wide variety of use cases, it is still too early to say whether these use cases can be scaled across broader customer bases.” Indeed, most of the validated use cases—79 of 91—came on the clinical side, which includes population health, clinical research, patient engagement, clinical decision support and clinical education.
Notably, one of the analytics platform vendors with AI infrastructure that was studied was IBM Watson Health, which customers reported using the company’s technology for a variety of clinical use cases. IBM Watson, of course, is an AI supercomputer that was launched into the world of healthcare just a few years after it won in Jeopardy! against record-setting champions in 2011. But along with the popularity of Watson has come intense scrutiny, especially in the last few years. As covered in one of our Top Ten Tech Trends last year, an investigation into IBM Watson by STAT News revealed that the artificial intelligence platform has not lived up to its potential.
The report also offered guidance on best practices within healthcare organizations adopting AI, and for vendors seeking to offer the best capabilities, support and service. “While technology is important, success with AI is perhaps even more dependent on an organization’s operations and change management,” researchers noted.
Best practices that come from some of the industry’s most successful AI users, per the report, included: embedding AI into the workflow, rather than creating extra hoops for end-user clinicians to jump through; bringing together experts on AI, data science, modeling, analytics, and subject matter; and taking ownership for driving change management and operationalizing insights.
What’s more, best practices that were reported by vendors’ most successful AI customers included delivering comprehensive services for an AI platform (such as support service resources like client success managers, dedicated data scientists, and field engineers); providing strong customer training; and being a humble, active partner while being responsive to constructive feedback.
The report also examined current misconceptions about AI in healthcare, such as the myth that building models is the most time-consuming AI task, when in reality, the time and effort it takes to prepare the data needed to test and build the models is a more pressing issue.
Further, assuming that the AI model will “run itself” once it’s built is another common misnomer, as is the belief that there are turnkey AI solutions that can drive outcomes. The report stated, “If you want tangible outcomes from the data, you need to consider operational aspects, like how many departments, resources, and facilities across the care continuum need to be involved.”