How 3M is using AI to reduce tech burdens in the revenue cycle

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Q: Moving to AI in the revenue cycle, there is a lot of noise around AI. What do you consider real and what is hype?

JS: What is real is that AI is happening, from healthcare tools and technology to the wider spaces like self-driving cars and consumer applications.  In healthcare, there are very real models developing to identify clinical understanding. Creating a better picture of potential diagnoses and conditions through AI technologies helps drive efficiency in the revenue cycle so that we can boost productivity of those who are evaluating cases for clinical documentation improvement, coding or quality. All of that is real, and it is driving AI capabilities within the health system.

What is hype is maybe the perception that AI will solve all of our problems immediately, and that we’ll be able to automate at the touch of a button things that are handled today by people with clinical training and knowledge who are reviewing the case. The idea that AI is just going to remove that need and eliminate the human element is hype. There will always be an interactive relationship between what AI can do to process information and how that intelligence aids a human reviewer, or user who applies their critical knowledge to what AI is telling them, to then do their work more efficiently. Will we get to a stage where more automation is possible? Absolutely. But I think right now there is also a very important relationship between AI and the human element.

Q:  What is 3M’s mission and what differentiates it from other companies?

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JS: What makes us unique is that we are a partner not only to the provider community in health organizations and hospitals across the country, but we’re also very tightly aligned with the payer community. We work a lot with CMS, state Medicaid agencies and commercial payers. So, in a sense, we have to walk a very balanced line on making sure that in the revenue cycle phase we are complete, compliant and accurate. A provider should get paid every dollar they deserve, and a payer shouldn’t pay a penny more than appropriate. We have to walk that fine line of accuracy and be a trusted third party to our partners. We don’t pick sides in that regard.

In doing so, we have our business structured so that we have solutions for the revenue cycle based on the provider organizations. We also sell some of those solutions into the payer space and drive their initiatives more around value-based care. What has expanded that mission in the past year and a half is our acquisition of M*Modal to be part of our organization. This really drives a focus on the clinicians themselves. Our speech and AI powered clinician-assistive solutions drive our mission of creating time to care by make bringing clinical intelligence in their workflow for more accurate documentation..

Q: In revenue cycle, the C-Suites’ dream is fully autonomous clinical documentation capture, coding and clean claims without denials—will this ever occur?

JS: This is a dream we share. For us to really be able to deliver automation, I see it starting in two ways. The first is pure automation—understanding clinical documentation and codes to realize a direct to bill model. There are segments in the revenue cycle where we are using this type of automation, such as radiology, using AI technologies to read documentation and apply codes straight to a claim out the door. We would say there’s a larger slice of the services in healthcare that could qualify for this model today, so there is potential to expand. Simple, standard procedures should move in this direction with the goal to increase the range of services that we can automate. The other is, automating the priority factors for individuals who need to review a case so that they are reviewing cases more by exception rather for every single one. There is AI that drives prioritized case review, which will continue to become more accurate and standard practice.

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Q: Anything else to add?

JS: The last comment I would make about automation is to deliver true automation with a clean claim that is acknowledged and paid by the insurer—I don’t know that it will ever be achieved until there is a more cohesive or better sharing of information between the provider side of the house and the payer side of the house. Arguably today, when a case is put through the revenue cycle on the hospital side and it’s put out on a claim, the same processes of review and recoding the case often happens on the payer side. The vision is that true automation would bring those sides together, to where providers and payers are really looking off the same sheet of music, and they’re able to see and review the evidence delivered by AI that justifies a claim in the revenue cycle space. When we can reach that point—and AI will help us get there—then the process is less of a back and forth affair between payers and providers and it becomes a system of record for agreement between the two.

More articles on artificial intelligence:
7 ways hospitals use robots during the pandemic
MIT uses supercomputer to fast track development of COVID-19 treatment drug
UC San Diego uses AI to help detect pneumonia, COVID-19 from lung X-rays

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