Healthcare Payments

5 Ways Machine Learning and RPA Work Together In The Revenue Cycle

Post by
Justin Nicols

Healthcare providers are investing millions of dollars into automation. For many health systems, their investments into RPA haven't delivered as expected. There are many reasons why RPA projects fail. One key reason is that RPA projects are started before there is a solid understanding of the root causes of problems (or in reverse, without identifying the true scope of opportunities). Machine Learning can mitigate these oversights and set RPA projects up for success. 

RPA and Machine Learning are quite different. RPA automates well-defined manual processes while Machine Learning detects and predicts patterns in data to drive strategy. But when combined, these two approaches have a powerful synergy that enables healthcare providers to truly leverage their payments data to drive meaningful improvements within the revenue cycle. 

ML: The Heavy Lifting
Automating processes without a clear understanding of the root causes of revenue cycle challenges is like giving a patient antibiotics without an understanding of why they are ill. It’s an easy course of action that *could* provide results, but it could also make problems worse, or have a negligible impact. A wiser approach is first developing a solid understanding of payments data to pinpoint revenue cycle problems and opportunities. Machine learning does this heavy lifting of organizing and normalizing data and identifying meaningful trends. Machine Learning uncovers underlying patterns and recommends the best courses of action to get desired results. Machine Learning gets at root causes  — highlighting where automation makes sense and can drive meaningful improvements.

When RPA and Machine Learning are combined, you move beyond simply automating manual processes to utilizing complex data to make decisions and take action. ML does the heavy lifting to leverage data. RPA puts ML to use. 

Intelligent Automation In The Revenue Cycle
Intelligent automation, the combination of Machine Learning and RPA, is becoming the status quo in many industries. In healthcare payments, there is ample opportunity to leverage intelligent automation.

In the revenue cycle, combining machine learning and RPA can optimize workflows, fine-tune resource management and ultimately, improve payment outcomes. Machine Learning can determine the next best action that should be taken with a claim, denial or patient account — and RPA can make executing on that recommended action more efficient. Here are five examples of how RPA and ML can work together, intelligently, in the revenue cycle:

  1. Denial Prioritization - Machine Learning can rank-order claim denials based on overturn potential. RPA can then gather information and fill fields to resubmit the appropriate claims.
  2. Payment Plan Provisioning - Machine Learning can determine the best-fit payment plan for a patient. An RPA-based text bot can send an SMS text message offering the payment plan to the patient.
  3. Denial Prevention - Machine Learning can proactively detect claims that are likely to be denied, RPA bots can resolve or route those claims for manual review & processing.
  4. Patient Financial Engagement - Machine Learning can determine the likelihood and timing around a patient successfully resolving their account, as well as identifying the most cost-effective option for follow-up efforts (i.e. internal team or external vendor). In many cases, RPA can initiate appropriate follow-up efforts.  
  5. Write-Off Management - Machine Learning can leverage historical reimbursement trends to predict write-offs. RPA can initiate form fills for write-offs to lessen human time/touches.

There are myriad of applications for ML and RPA partnerships in the revenue cycle — clear opportunities to go beyond automation and move towards intelligent workflows and data-driven strategies.

RPA on its own makes revenue cycle processes more efficient, but the addition of machine learning makes processes intelligent. An intelligent revenue cycle leads to less waste and better revenue capture.

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