Presented by Change Healthcare
AI is a crucial tool for healthcare providers today, from revenue cycle management to operational efficiencies and more. Don’t miss this VB On-Demand event for a look at the ways AI is transforming how clinics deliver patient care.
AI is transforming how clinics deliver healthcare, at every point of the the healthcare revenue cycle, with the sheer amount of valuable data that healthcare clinics produce, plus the maturity and capabilities of AI products in the market now. Not only is it delivering an increase in efficiency in the back office, it is helping healthcare providers serve the patient better.
“Everybody expects good healthcare, but when it comes to revenue cycle management and the billing process, that’s particularly where patient surveys fall down,” says Tina Eller, VP of revenue cycle management (RCM) tech-enabled transformation, Change Healthcare. “Automation and tech have been able to help us do our jobs better, be more knowledgeable and connect the dots along the cycle. The patient experience has been something that we’ve been able to focus more on, while keeping the margins and collecting the cash. That’s the primary function of RCM.”
From the point of registration and scheduling, to the point of resolving the claim and adjudicating it on the back end, bringing AI into existing workflows and algorithms, helps healthcare providers expand their capabilities far beyond what was possible with static repeatable processes or bots. The other major benefit is how AI can help reveal deeper trends among user behaviors and in the workforce. Critical analysis of the data that clinics produce can propel improvements and optimizations, uncover areas of opportunity for standardization, and even point out where one-off projects can solve a business problem.
“The impact of AI is being felt across the entire revenue cycle,” says Kaushik Roy, VP of AI product management, Change Healthcare. Just for example, he explains, prior authorization “is a market opportunity in the hundreds of billions of dollars in terms of how much it can save.”
Increasing revenue without raising the cost of care
Much of the benefit of AI, when it’s applied to RCM, is where it helps eliminate the overhead of manual labor in repetitive tasks and processes — especially those that can be switched up to run in parallel, shortening the revenue cycle. And as it continues to ingest data and learn, it shortens the time it takes to learn the next task, since it can often run in the background without disrupting ongoing work. Every new project or process can produce results faster than ever before, since testing can be done in cycles — and the fact that testing becomes table stakes is among the biggest ways healthcare delivery becomes more efficient and effective as the bottom line grows.
“We can truly make hard wired choices and changes, as opposed to throwing darts at the wall,” Eller explains. “Because AI workflows are based on historical data and a lot of cycles of evolution of learning, it can also guarantee that you have that best practice model in place. It constantly learns and fine tunes as you go — as you evolve and as your patient population or the revenue cycle demands.”
For instance, a clinic is able to far more easily pivot to handle something like this year’s No Surprises Act, and incorporate compliance into the workflow in an accelerated way.
Best practices for AI implementation
AI is simplifying workflows, but it’s a complex technology to put in place, especially for healthcare organizations. The most essential part of the equation is strong partnerships, Roy says, because the combined powers of data scientists and engineers with the employees who have the required domain and business knowledge is crucial.
“One thing we’ve learned over the three years we’ve been working with Kaushik and his team is that the domain expertise combined with the technical and the AI expertise is critical,” Eller explains. “We were building models over those nuances that could have really taken the model left. Having all the domains together as partners, testing, walking through scenarios, identifying outliers and making sure that the outcomes are at the right percentage we need, gave us the confidence we needed.”
This ties into the eternal build-or-buy question, which can be tricky when an organization has expertise in-house. But knowing what your teams excel at, and where help can make all the difference is the turning point. If you don’t have a mature AI shop already, outside expertise can be tapped while you build in-house talent rosters. But starting net new can be costly, and unless you have the appetite for a multi-year investment without return, that can be challenging.
“Building AI requires a lot of infrastructure, especially data infrastructure and the legal framework,” Roy points out. “You need to have the data access usage rights and so on. That may not be possible for a lot of mid-sized and definitely smaller clinics. That’s where you have to leverage vendors or other expertise.”
A staggered rollout plan, patience as the algorithm learns and setting expectations for the end users about how AI models work is also key. It requires time and commitment from all sides of the AI implementation project to get the models into shape where they can be deployed successfully. It’s also critical, Roy says, to choose your use cases wisely, narrowing the focus down to a very specific problem and applying that patience and commitment.
“Pick a small problem, whatever it is, but an important problem with well-defined value, then establish that success and build on it,” he says.
For in-depth insight on the ways AI is changing each step of the revenue cycle, a look at implementation challenges and opportunities — including regulatory and industry concerns, plus a glimpse at emerging AI technologies that will continue to transform healthcare, don’t miss this VB On-Demand event!
- How AI can support financial performance and operation efficiencies
- Case studies demonstrating how AI technology improves clinical workflows
- Ways AI can increase net patient revenue (NPR) for providers
- Exploring NLP-based medical entity extractions
- Tina Eller, Vice President of RCM Tech-Enabled Transformation, Change Healthcare
- Kaushik Roy, Vice President of AI Product Management, Change Healthcare
- Art Cole, Moderator, VentureBeat