Getting the Greatest ROI from Revenue Cycle Automation

March 10, 2022

Two years after operating margins dipped into negative numbers and YOY revenues decreased as much as 50%, hospital leaders are looking for solutions to achieve a financial 180. They believe automation can help them get more from their revenue cycle; according to KPMG, early adopters think artificial intelligence (AI) will enhance numerous areas such as electronic health record (EHR) management. More than  90%  say it will improve the patient experience.

Automation has the potential to transform the revenue cycle, enabling significant improvements from end to end, but not every hospital will see major benefits. Overcoming RCM complexities may prove difficult, especially if hospitals succumb to common missteps.

Missteps threaten automation ROI

With RCM’s inherent challenges – evolving consumer-driven demands, integration issues, complexities from big data, a third-party payer system and the shift to value-based care – automation can seem daunting. Healthcare financial leaders might struggle to implement and manage automation in a way that delivers the expected ROI. A number of common missteps can impede results, creating additional work and reducing expected returns.

Most common missteps in RCM automation result from one key error: Not taking a 20,000-foot, holistic view of the revenue cycle and developing a strategy for scalable, end-to-end intelligent automation.

It might seem easier to address tactical or singular pain points, or more tempting to lean toward exciting “quick-win” solutions. But sustainable, impactful results come from using a combination of technologies such as machine learning (ML), robotic process automation (RPA) and natural language processing (NLP) to create a layered intelligent automation (IA) approach.

The IA ecosystem and why it’s important in RCM automation

A well-developed IA strategy lets you layer different automation technologies, thus increasing the number of processes you can automate. Within the complicated workflow of the revenue cycle, this fuller complement of technology allows hospitals to use the ideal type of automation for each process or task rather than having to rely on a “one size fits all” approach.

To provide even more value, we leverages an IA ecosystem. The IA ecosystem uses the layered approach but also includes an integrated workflow platform for an added critical benefit: digital and human workforces can work seamlessly alongside each other. Together, these two benefits enable hospitals to truly move the needle in multiple KPIs such as cost to collect and net patient revenue rather than making marginal improvements.

In our IA ecosystem, the integrated workflow platform orchestrates revenue cycle workflow and ensures seamless hand-offs. It uses technology “levers” such as RPA, maximizing the efficiency of these technologies as well as human workers. While most typical RCM processes can undergo full automation, the integrated workflow platform knows which tasks need human intervention. It directs tasks to the best worker – either a human or a digital lever – and uses exception handling to route requests correctly.

In highly detailed workflows with numerous variables – for instance, claims management – the integrated workflow platform is key to achieving the greatest efficiency and getting the most from automation technologies. Here’s a closer look at three other technologies that are well suited for revenue cycle automation and included in our IA ecosystem:

Robotic process automation, or bots, are digital workers that navigate a system’s user interface as a human would. With RPA, you can automate highly time-consuming and labor-intensive work. RPA can perform routine, structured revenue cycle tasks with greater accuracy and speed than a human. Ideal revenue cycle tasks for RPA include posting payments in a patient accounting system and downloading and attaching medical records to an appeal.

Machine learning (ML) and expert rules is a form of AI that uses algorithms to understand patterns in structured data and predict outcomes or actions. It can identify the most effective way to complete a transaction, ensure processes are carried out accurately, and learn where and how errors typically occur. ML can make numerous types of valuable predictions in RCM, such as the typical A/R follow-up activity and the likelihood of an account write-off.

Optical character recognition (OCR) & natural language processing (NLP) can extract valuable information from unstructured data such as medical records, scanned documents and audio recordings. By replacing time-consuming work like sorting, transcribing and data entry, it can save revenue cycle staff a substantial amount of time. In addition, because it reduces the need for transcription, it lowers opportunities for error.

Understanding your automation levers

Knowing what type of automation technology to use and which tasks to automate can be challenging. To get the most ROI, remember you’ll need to apply IA to large-scale processes across the comprehensive revenue cycle. Also, when deciding what type of automation technology to use – or what lever to pull – you’ll need to consider both the task and the information you’re working with.

For example, with unstructured data such as an image file, medical record or clinical chart, OCR/NLP technology is ideal for preprocessing data and feeding it into a subsequent automation. With structured data, ML can assess trends and determine the best way to complete a transaction. Lastly, for routine manual transactions such as insurance verifications and payment postings, RPA may be the best choice because bots can quickly perform repetitive, routine tasks.

The value of an IA ecosystem in uncertain times

Intelligent automation is much more than any single lever. An IA strategy that allows you to tackle each distinctive part of the revenue cycle accurately and proficiently while also maximizing human efficiency can deliver many benefits, including reduced costs, improved decision making, increased staff productivity and a better patient experience.

Using a fully developed IA ecosystem not only facilitates speed to value and high ROI, but also ensures you can pull additional IA levers or automate more tasks as needed over time. As your health system grows – either organically or through consolidation – you’ll have a ready-made strategy and technology platform to scale appropriately.  With the right IA strategy, efficiencies and gains will amount to much more than just financial savings and healthier profit margins. In addition, you’ll be able to increase patient, provider and staff satisfaction – and enhance your competitive differentiation.

To achieve these gains, you’ll need a partner with deep expertise in both IA and RCM. This partner should have a fully developed, proven IA ecosystem and framework for effectively managing your digital workforce after implementation.

R1’s proven, extensive IA capabilities have helped hospitals achieve benefits such as a 20% reduction in cost to collect and 50% reduction in denials. We automate more than 32 million tasks annually.  Interested in learning more? Click here to start a discussion about how IA can reduce your costs, increase your profit margins and improve your team’s efficiency.

Author Bio: Philip Milsom is a Regional Vice President at R1 RCM.

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