Beyond RPA: What the Next Phase of Revenue Cycle Claims Process Automation Should Look Like

Claims process automation

The Gist
It’s not enough to only have a solution for claims process automation in place — it must also be effective and built for purpose. As insurance companies and providers alike incorporate more automation into their claims processing workflows, selecting a solution that can address persistent challenges and provide key benefits is critical. A solution-driven by AI and machine learning, as opposed to more fragile RPA, will grow along with your organization.

Effective automation can transform businesses.

The key word in this conversation is effective. Not all types of automation are equal, and more basic forms can fail in the complex and dynamic environments that are standard in healthcare.

Finding the best claims process automation solution, one that can capably adapt to changes and successfully support an organization throughout its revenue cycle management efforts, is in every provider’s best interest. Let’s take a closer look at the value of effective automation, the features that separate leading solutions from other options, and how claims processing automation supports productivity and drives revenue.

Claims Process Automation: Beyond Insurer Use Cases

The claims process is crucial to the continued stability and success of healthcare providers. It’s also extremely detailed, intricate, and subject to frequent changes. Add in a broad increase in claims denials in recent years — the American Hospital Association found 89% of hospitals and health systems faced a rise in rejected claims from 2017 through 2019 — and the importance of an efficient and reliable system for claims management becomes even more clear.

Read more about why claims denials are at an all-time high.

Insurance carriers have claims processing needs of their own and are increasingly turning to automation combined with human support to streamline their processes, as Ernst & Young explained. While businesses in the insurance industry have different priorities than healthcare providers, the value of effective automation is clear.

More effective allocation of employee time and resources and reduced costs due to increased operational efficiency are broad benefits that come along with artificial intelligence (AI) and machine learning (ML). These powerful advantages are also accessible to hospitals and health systems through solutions that specifically address common challenges, like reducing initial denials and improving first-pass payment rates.

“Use of automation is increasing throughout the healthcare space,” says Sean Lara, head of strategic partnerships at AKASA. “Providers shouldn’t simply want to avoid being left behind with less-effective claims processing workflows. They need to seek out an automation platform for the long-term, one which can not only handle workflows of increasing complexity, but can also withstand the dynamic and exception-driven nature of the healthcare revenue cycle.”

The Most Persistent Challenges in Claims Processing

The complex nature of medical claims processing can lead to a variety of concerns, including:

1. Achieving and maintaining high clean claim and first-pass payment rates

Clean claims, those that are free of errors and expected to be ready for processing and payment, are often viewed as a key metric by hospitals and health systems. About 78% of such organizations measure the difference between clean claims and initial denial rates to forecast performance, according to a recent AKASA survey.

Organizations that shift their perspective around performance forecasting may realize stronger results, however. Instead of measuring based on assumption, as is the case with clean claims, companies should place a higher degree of focus on their first pass payment rate — the number of claims paid completely and accurately on their first submission. This accurate measure of performance can provide more actionable insight for revenue cycle leaders.

2. Optimizing workflows and realizing better utilization of employee time

Changes to payor requirements, updates to billing codes, imperfect collection of patient information, and other factors all contribute to a complicated claims processing environment. Unfortunately, some commonly used tools can add to the complexity.

For example, robotic process automation (RPA) is commonly used to support claims management workflows, but can often be ill-suited for this task. RPA bots do not continue to function as intended when codes change or an interface or webpage is updated. Human intervention (often with a high price tag) is needed to correct and update the RPA bot to address these administrative tasks.

With the need to use dozens or even hundreds of RPA bots to effectively support claims processing, this approach ultimately requires a significant investment of time, whether directly from staff or on the part of a consultant or service provider.

The good news: Robust AI and ML have the power to work around these issues, and can support a better claims management process through intelligent automation.

Comparing Automation Options and Alternative Approaches

Understanding your revenue cycle team’s options for optimizing claims management can help it more confidently chart a successful path forward.

Outsourcing

Contracting with an outside provider to manage the claims cycle offers quickly available and competent professionals, predictable pricing — although additional fees can become a financial concern — and an alleviation of many burdens on staff that can come with inefficient internal processes.

However, it can also create more cybersecurity and HIPAA-related compliance issues, and extend payment timelines.

Robotic Process Automation

RPA is an improvement on wholly manual data entry processes and other tools for claims processing, but is fragile and not particularly well-suited for the dynamic environment of claims management. Workflows are prone to break when a single element in a process changes, and providers either have to commit to a high degree of maintenance or suffer the consequences. RPA also generally requires an additional expense, in the form of consulting, to support its systems.

Artificial Intelligence and Machine Learning

AI and ML add value to your organization over time. These technologies quickly learn how to handle standard claims. When coupled with a human-in-the-loop process, they can support the outliers and edge cases common in the revenue cycle.

That’s how AKASA works. When an unknown issue crops up, the technology routes it to a revenue cycle expert. The expert solves the issue and the technology learns. Afterward, the system can handle similar issues on its own, reducing the need for future staff involvement.

The unique advantage of intelligent automation empowers proactive and resilient workflows that can identify and address concerns before they turn into larger problems.

What Should AI-Driven Claims Process Automation Provide?

Claims process automation

A high-quality, AI-driven solution for claims process automation solution should offer:

  • Specialization: The solution should be designed for claims processing and providers specifically, aligning with their needs, goals, and operational environment.
  • Adaptability: AI and ML offer value by changing, growing, and learning, which leads to better claims outcomes with only limited human involvement for edge cases and outliers.
  • Scalability: The best claims process automation platforms scale along with a provider as its operations diversify and grow.
  • Ease of implementation and upkeep: Automation platforms based in AI and ML require oversight for deployment and maintenance, but those needs should be modest — especially when compared to the commitment required of more fragile RPA.

With the AKASA Unified Automation™ platform, hospitals and health systems can count on dependable results driven by AI and machine learning, turning a complex and dynamic process that otherwise requires so much staff time and attention into a much more manageable and successful workflow. Your team can focus more on the activities crucial to generating revenue.

To find out more about the enduring advantages an advanced automation solution driven by AI and ML can provide, schedule a demo with AKASA.