The way AI has transformed healthcare RCM is remarkable. But are hospitals using it the right way?
Unless you’ve been taking a Rip Van Winkle-length nap, you’re well aware of AI’s growing influence in healthcare revenue cycle management (RCM).
In only a few short years, the integration of AI into healthcare has accelerated at an astonishing pace. In the RCM space, AI initially focused on simple, rule-based, repetitive tasks like basic billing, coding, and sorting claims.
Not anymore.
Today, AI can converse with patients, distill huge swaths of data into a paragraph of actionable insights, and generate its own content. Even more impressive, it can do it all with little to no human intervention.
Meduit’s SARA (Supervised Autonomous Revenue Associate) started in 2020, executing 12 basic RCM-related tasks. Now, SARA can do more than 200, from uploading medical records to analyzing claim denials, with no ceiling in sight.
For healthcare executives, the question is no longer whether to use AI but rather how to maximize its many functions within a single, cost-effective solution.
As AI evolves and becomes more complex, deploying a comprehensive solution that synchronizes processes and optimizes RCM results becomes a greater challenge for healthcare organizations. But it’s a challenge worth tackling.
HARNESSING AI
As hospitals and health systems consistently face financial pressures caused by workforce shortages and evolving payer strategies, AI has been a remarkable resource. Almost singlehandedly, it has allowed savvy providers to navigate RCM obstacles and remain financially viable.
While AI started as a one-trick pony, handling low-level, repetitive tasks, it now operates across a diverse spectrum with specialized applications that can be integrated throughout the revenue cycle. These include the following:
Low-level Essentials
AI’s ability to execute basic coding, processing, and billing at remarkable speeds frees employees to take on higher-level tasks. This functionality is absolutely critical in a market lacking enough experienced RCM professionals.
Predictive Analytics
By analyzing historical payment data, trends, and patient behavior, AI can identify accounts that are most likely to pay their outstanding balances, saving RCM employees from the wrong rabbit holes and increasing the likelihood of successful outcomes. Using the same methodology, AI can forecast claim denials before they happen, which allows flawed claims to be fixed before submission.
Conversational AI
Conversational AI is transforming patient engagement with 24/7 self-service options. The technology allows patients to access account details, make payments, and set up payment plans, all without needing a live representative. On the provider side, they can collect revenue around the clock without hiring additional staff.
As conversational AI evolves—at Meduit, we analyze every incoming call to consistently refine our best practices—it will continue to enhance the patient experience, drive timely payments, and enable employees to use their time more effectively.
Generative AI
While not widespread (yet) in healthcare RCM, AI’s near-future capability includes generating written correspondence. For example, as payers leverage AI to deny claims, providers can use it to quickly draft letters of appeal, speeding up a process that favors payers for the longer it goes. AI can also correspond with patients, sending return emails to submitted questions or requests. As AI’s capacity to create its own content expands, it will reduce the burden on RCM staff while increasing the likelihood of accelerated cash flow.
Every segment of AI is valuable on its own merits. However, treating each variety as a separate solution creates redundancy and inefficiencies. Plus, managing multiple vendors and platforms mitigates the impact of all the AI iterations working together. Instead, it creates a scenario where the parts are greater than the sum.
As AI becomes both more sophisticated and complicated, providers must simplify their approach and consolidate their AI solutions to optimize effectiveness.
THE CASE FOR A UNIFIED SOLUTION (vs. a Fragmented Approach)
A single platform minimizes the need to manage and integrate data from multiple systems.
One solution/platform makes sure pertinent data moves efficiently from one area of AI functionality to the next. For example, an AI bot specializing in deciphering unstructured data like incoming emails can pass requests along to another bot that specializes in crafting email responses.
Engaging with one, instead of multiple RCM partners typically lowers fees and administrative costs.
As new AI capabilities emerge in the near future, which they most certainly will, a unified solution is designed to adapt to the changes easily. This future-ready approach ensures that a hospital or health system’s investment in AI remains relevant and effective over time.
PUTTING IT ALL TOGETHER
As AI becomes more elaborate, it beckons for simpler implementation.
A single, integrated AI solution ensures that healthcare organizations can harness the full power of AI without the downsides of fragmentation, which include data redundancy, increased administrative burdens, and higher costs.
For healthcare executives, leveraging AI as a unified strategic asset is the best way to optimize its full capabilities and advantages.
Getting the most out of AI for the least amount of exertion and expenditure is truly the wave of the future.
Meduit’s AI-driven solutions span the expanse of the revenue cycle. They eliminate the complexity of managing multiple vendors and ensure seamless, optimized RCM performance.
Contact us today to see how a unified AI approach can transform your revenue cycle and inspire the RCM performance you’re looking for.