Industry Voices—Supercharging revenue cycle management with AI-fueled workflow automation

Stethoscope on top of bundles of money
AI-powered workflow automation isn’t new, but it’s come a long way since the early 2000’s, when the idea of leveraging analytics technology to drive change was still in its infancy. (Getty Images/Wavebreakmedia

The market for smarter healthcare technology is soaring, and artificial intelligence (AI) technology is one of the most vibrant and exciting subsets. It’s defining the futures of most industries. The AI healthcare market, for example, is expected to expand from $2.1 billion to $36.1 billion in 2025, according to just one projection.

Much of what’s happening with AI is clinical: We’re leveraging the most powerful machine-learning algorithms on the planet to optimize clinical workflows, coordinate care and practice precision medicine. All good stuff.

But what we’re not doing, especially in ambulatory settings, is using AI to make our financial workflows smarter. This is a problem because revenue cycle management (RCM) is growing more complex as payers adjust to value-based care models. When healthcare organizations can’t keep up with these complexities, everyone suffers.

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Smarter RCM  

AI-powered workflow automation isn’t new. But it’s come a long way since the early 2000s, when the idea of leveraging analytics technology to drive change was still in its infancy. But most provider groups haven’t thought about using software that can potentially transform their workflows. It’s expensive and historically has produced mixed results.

But these perceptions are outdated.

Over the past few years, advances in machine-learning technologies have gone far beyond mining data to identifying trends and proposing actions. It’s now possible to analyze data to derive more granular insights such as the probability a patient with certain attributes (e.g., female, Medicare beneficiary with multiple chronic illnesses) will pay her bill within 30 days.

RELATED: Providence St. Joseph Health acquires revenue cycle management blockchain startup

To do this, workflow automation technology taps into sophisticated, back-end algorithms that analyze data— crunching raw numbers, claims and codes for the purpose of isolating patterns. Modern tools take this process one step further by offering actionable insights to practices.

For many medical groups struggling to get by, this technology is transformative—and it no longer comes with a price tag out of reach for smaller practices or specialties.

Automation in Action

For some, it may be hard to envision AI in action, so it’s helpful to compare physician practice workflows—with and without workflow automation tools at our disposal.

Without workflow automation technology, in-house billing staff begins each day with a long list of claims that need to be processed, denials, and/or unpaid balances. It’s always a guess as to how to move forward: Which claims should be worked first, second and third? How long do we attempt to resolve a claim? In the process of staff second-guessing, time and money are lost.

In a traditional practice, the business stuff can drag us down. Billing staffs spend much of their day on hold, feeling bad when they fail to recoup payment or resolve lingering issues. They’re backed up and overwhelmed. The potential for mistakes increases.

But with workflow automation that taps into machine learning, claims are prioritized from the moment we sit down at our desk. We are told which claims should be worked first, second and third based on expected reimbursement and other variables (e.g., likelihood of resolving a claim). By analyzing dozens of factors simultaneously, machine learning can organize information, detect patterns and predict outcomes (e.g., such as whether processing Claim A will yield a higher return than Claim B). This enables our in-house staff to work smarter. They can sit down at their desks and immediately deal with the most important claims first without confusion or stress.

RELATED: Trinity Health begins major restructuring around its move to standardized billing system

Not only does accuracy improve, but denials and underpayments are reduced. A practice’s rate of bad debt is curbed. AI has empowered our technology systems to deliver unprecedented insight into larger problems—productivity, patient behaviors, collection barriers and more, going a step further to reveal the role they play in a practice’s financial solvency.

And we’re just getting started. The potential impact AI-driven workflow automation has on practices or specialties is infinite. 

Practices that traditionally couldn’t afford such advanced technology can now scale, bring on more doctors or specialties and explore options that they feared due to potential RCM challenges like telehealth. 

While there may be some initial resistance to technology-driven changes, it’s usually temporary. In our experience, not one practice employee on the RCM side has ever expressed a desire to go back to the old way of doing things.

This is an important point to emphasize. Claims are always going to exist and we’ll always need to process them. And issues will still arise. But by replacing traditional workflows with automated workflows, physician practices are free from the heavy weight of lengthy RCM to-do lists. We can instead focus on delivering value and quality to every single patient we see.

Matt Seefeld is the executive vice president of MedEvolve, Inc.

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