A KLAS report released this week on healthcare artificial intelligence tools found that while customers are highly satisfied with ClosedLoop.ai, a high number of Jvion users have jumped ship in the past few years.
The report also included case study summaries from several organizations about their AI use cases and achieved outcomes.
“Good opportunities for AI are scenarios that require a lot of data,” said one chief information officer quoted in the report. “People should focus on use cases where they need AI to learn a lot of data and predict various conditions. Not everything is a good use case for AI.”
WHY IT MATTERS
Artificial intelligence holds great promise for the healthcare industry, but it also has limitations. By interviewing customers about how they use AI, KLAS researchers examined the range of uses for a variety of groups.
KLAS researchers found, for example, that payer and provider organizations use ClosedLoop.ai to improve risk scores, decrease readmissions, close care gaps and reduce claims denials.
The vendor’s reported strengths include strong implementations and high involvement in change management.
“One potential hindrance is the need for technical resources within the customer organization to support the solution and monitor the models,” said the report.
Customer satisfaction with Health Catalyst has also increased over the past year, as the company has stepped up guidance efforts as to where clients should focus their AI efforts.
“Customers report that this guidance enables them to focus on the right populations and problems. They speak highly of the vendor’s expertise and willingness to help them achieve their goals,” said KLAS researchers. “Newer AI customers in particular have experienced close partnering during implementation.”
Epic and Cerner, meanwhile, also offer prebuilt models – but challenges related to ease of use can hinder outcomes for both, clients report.
At Epic, for instance, researchers said that “customers feel the prebuilt models are generally well integrated into their workflows. However, many report that testing these models and getting them to a state where they are delivering outcomes requires a significantly larger lift than expected.”
Meanwhile, Cerner customers say they have difficulty getting models up and running.
And when it comes to Jvion, which KLAS characterizes as the previous market share leader, researchers found that a high number of customers have departed, citing pandemic-related financial constraints and a lack of worthwhile outcomes.
“Organizations that are staying say that to make the system impactful, they need more proactive support to promote adoption and turn the models’ data into actionable insights,” said the report. “They also want the prescriptive outputs to better integrate into EMR workflows.
“A couple of respondents say progress is finally being made following major leadership changes over the last couple of years,” researchers added.
THE LARGER TREND
Health systems and organizations have been turning to AI for several innovations in care delivery, including radiology, sepsis detection and cardiology, to name a few.
But AI has its limitations, as experts have routinely pointed out – tools should be designed to fit a user’s needs, and not the other way around.
ON THE RECORD
“The biggest challenge with AI is operationalizing the models,” said a vice president quoted in the KLAS report. “We could create models left and right all day long, but they don’t mean anything until they are put into operation and can make a difference in decision-making.”
Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Email: [email protected]
Healthcare IT News is a HIMSS Media publication.
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