A KLAS report released this week found that few organizations have settled on any particular artificial intelligence vendor as their choice going forward. Instead, said the report, most are using a mishmash of software and tools in order to fulfill their AI and machine learning needs.
The report authors surveyed the AI purchase decisions of 47 provider and payer organizations to examine which vendors are being considered and chosen, which of them are being replaced and why systems are choosing specific tools.
“The top factor driving purchase decisions in healthcare AI is expertise (i.e., healthcare-specific knowledge as well as ML and data science expertise),” wrote the report authors.
Conversely, organizations cited functionality, price and product maturity among the top reasons for not selecting specific vendors.
WHY IT MATTERS
The report found that Jvion, a Georgia-based predictive analytics company, has both high visibility and a large customer base. However, nearly a quarter of respondents reported overall dissatisfaction – a decline since last year.
“These clients say that despite excellent efforts from Jvion executives, it has taken longer than expected to achieve results and has been challenging to realize an ROI,” said report authors.
As far as electronic health record AI tools go, Epic’s Cognitive Computing is the most widely adopted, according to KLAS. The models, the report said, are used most often in clinical areas, but may not be a good fit for systems that aren’t prepared to drive outcomes on their own.
Cerner’s machine learning platform, meanwhile, is still in its early stages.
“Those who do look at it are mostly Cerner EMR customers – they report interest in the ability to customize their own models and ingest non-Cerner data,” wrote the authors.
When it comes to cross-industry AI giants, such as Microsoft, Google, IBM and Amazon, customers’ perceptions were mixed. Microsoft is seen as the strongest contender, with its healthcare offerings – such as Azure – signaling more industry expertise to many organizations.
Some respondents noted Microsoft’s seemingly strong security and data protection, but others said the company had a tendency to overpromise and under-deliver.
According to the report’s findings, Google has advanced capabilities, but less experience in the healthcare sphere; Amazon has a reputation for innovation, but unclear development strategy; and IBM has widely known AI technology with Watson Health, but sluggish real outcomes.
“Many respondents who have worked with IBM in the past report that good technology and sometimes-satisfactory results are dampened by overpromising, insufficient support, or low value,” said the report authors.
The KLAS report paid special note to imaging technology, which is a critical use case for AI and ML in healthcare. Aidoc, Viz.ai and Zebra Medical Vision have all developed FDA-approved imaging technology, and providers are starting to rely on their tools in clinical settings.
THE LARGER TREND
Previous KLAS reports have noted executives’ excitement about AI as an emerging technology.
Organizations say that clinical decision support is their most common use case for AI, while they’re likely to move toward using it for revenue cycle management in the future.
And at a virtual meeting of the U.S. Food and Drug Administration’s Center for Devices and Radiological Health’s Patient Engagement Advisory Committee this past week, Bakul Patel, director of the FDA’s recently-launched Digital Health Center of Excellence, predicted huge advancements in the arena of AI and ML.
“This new technology is going to help us get to a different place and a better place,” said Patel. “You’re seeing a great opportunity. You’re seeing automated image diagnostics. We have seen some advanced prevention indicators.
“Data is becoming the new water,” he added. “And AI is helping healthcare professionals and patients get more insights into how they can translate what we already knew in different silos into something that’s useful.”
ON THE RECORD
“Most respondents (64%) plan to or would like to use their AI solutions enterprise-wide,” wrote KLAS researchers in the new report. “Most of the 36% who don’t expect to use their AI solution enterprise-wide purchased their solution for very specific areas or use cases; others don’t currently have all the needed data loaded in the AI solution or would like to better deploy current models before expanding.”
“The 24% who would like to use their AI solution across the enterprise but don’t plan to state that they would like to achieve higher adoption of and better outcomes with their current use cases before applying AI to other areas,” they said.