Last week we sponsored HFMA's Revenue Cycle Conference in Austin, TX. The theme was "The Next Gen Revenue Cycle: Balancing Artificial Intelligence, Automation, and Digital Trends with Human Skill", so as an A.I. revenue cycle company this was right up our alley.
I spent the week with hospital CFOs and revenue cycle leaders, and I have one big takeaway:
- Capabilities ≠ Solutions
As more Artificial Intelligence solutions enter the healthcare space, CFOs and revenue cycle leaders are telling me they're tasked with distinguishing between (1) capabilities, (2) actual solutions.
Most of the people in these roles are career finance leaders who know the ins/outs of payer contracts and billing—but as admitted non-technologists with increasingly tight budgets, they're struggling to vet the new technologies they see in the market.
As I shared with several folks last week, here's how we think about the difference between a capability, and a solution:
Capability: a technical proficiency, or a tool.
Solution: a problem-removing and value-creating asset that is enabled by complementary capabilities.
"EHR integration" is an example of a capability—something like this is certainly valuable for enabling/powering solutions in healthcare, but in an isolated vacuum it does not inherently create value or drive action. Word of warning: often a "capability" is mistaken for, and mis-marketed as, a solution.
Alternatively, a solution meshes multiple capabilities to inherently create value and enable action (ideally, automatically) as a natural function of directing those united capabilities to address a problem that humans can't solve alone.
Take our prior authorization solution, "Lia", for example: we combine capabilities (tools) like machine learning models, system integrations, and workflow management to automatically administer prior authorizations in real-time so that humans don't have to.
If you've ever felt jilted after a technology purchase, you can likely trace the issue back to the fact that a vendor pitched capabilities as a well-rounded solution. Either the capabilities/tools themselves were under-performing, and/or they were not combined in a way that actually solves problems without disrupting human work.
And if you've ever purchased technology and then felt like you had to work harder just to make the tech work, you know just what I mean!
I've tracked the capabilities-solution divide in the Artificial Intelligence space for the past 15 years, and I'm seeing some cautionary trends playing out in healthcare currently that many folks were discussing at HFMA.
- "Is 'automation' a form of A.I.? Is it a solution? Is A.I. a solution?" Sometimes; Not in isolation.
- "Can a machine learning model solve [problem x]" In isolation, no—integrated with an actionable system, likely yes.
- "How do we distinguish capabilities from solutions?" Humans have to do more manual work when they're given just capabilities—when given solutions, humans get to do less manual work.
The dream-scenario is when a solution becomes a self-managing outcome—an asset that evolves to (1) deliver increasing value (more and faster revenue, more saved time and fewer costs, etc) and (2) consume less and less human input, direction, and concern.
Fortunately, Lia has become a self-managing outcome. The platform continually delivers faster authorizations with fewer denials at less cost to accelerate revenue—all without needing human input or course-correction. And we worked hard to make this possible.
Having learned a great deal in Austin about the challenges that rev cycle leaders are facing, and having shared my perspective on capabilities v. solutions with several folks, I realize that some actionable tools may be helpful. We've created a "Buying A.I." guide that is designed to help finance leaders separate capabilities from solutions, and I'm re-sharing that here in light of this being such a prevalent concern coming out of HFMA:
If you have other questions about capabilities vs. solutions, or if you're interested in "self-managing outcomes" like Lia, please contact us at team@digitize.AI.
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