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R1 RCM: Multi-Layered Technology Approach for Intelligent Automation Yields Financial Results

We recently covered the difference between digitization and electronification in Modernizing RCM with AI and why electronification is the choice more often made by the healthcare industry. It comes as no surprise that R1 RCM is onboard as well, spreading the message that integrating "intelligent automation" is the key factor that will yield financial benefits for RCM.

 Doctor reviewing different types of electronic documentation on a tablet.

In a recent article on HIT Consultant, Sean Barrett, Senior Vice President, Product and Digital Transformation, R1 RCM, provides his insights on intelligent automation and what its means for RCM and the financial welfare of healthcare, particularly in the wake of the COVID-19 pandemic:

Healthcare provider networks are experiencing enormous pressure to manage financial margins and invest in contactless patient experiences. With overall financial losses projected to exceed $323 billion as a result of COVID-19, a projected $200 billion in administrative waste due to revenue cycle inefficiencies, and increasing pressure to meet digital consumerism demands, it is essential for health systems to find ways to streamline processes, maximize their revenue cycles and cut costs. These industry trends are pushing organizations to invest heavily in automation solutions, such as artificial intelligence (AI) and robotic process automation (RPA) to alleviate operational and financial pressures.

Multi-Layered Technology Approach

Mr. Barrett stresses the importance of deploying an Intelligent Automation Platform that integrates different automation technologies including:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Optical Character Recognition (OCR)
  • Robotic Process Automation (RPA)

Individually, these technologies offer powerful solutions and can be applied to a variety of different use cases:

If working with unstructured data such as an image file or clinical chart, NLP and/or OCR technology needs to be deployed to pre-process or extract data; however, if you are working with large volumes of structured data, ML can be utilized straight away to assess trends and determine the best way to complete a transaction. When completing repetitive and routine revenue cycle transactions, such as adjustments, insurance verifications, and payment postings, RPA may be the right choice since it employs digital workers to perform these actions accurately and quickly.

Integration with workflow orchestration will enable humans to work harmoniously with these digital assets to "help health systems realize new revenue streams by improving net revenue capture, deliver cost reductions through automating time-consuming rules-based revenue cycle tasks and produce more predictable reimbursements."

Intelligent Automation for EOB Processing, Correspondence Management, and Business Intelligence

Mr. Barrett focuses on these three key challenges within RCM, as their unstructured data is a significant hurdle that intelligent automation is ready to overcome. Let's take a look at each challenge individually:

With NLP and OCR technology, organizations can convert unstructured data from files that are frequently utilized in healthcare – medical records, scanned documents, and audio recordings – into structured, normalized data.  For example, OCR can convert explanations of benefits (EOB) PDFs into a data table that RPA bots can then auto-post into patient records. NLP can extract clinical terms from an EMR note and provide key data elements to a machine learning model that will then assess the likelihood of medical necessity denials prior to adjunction. In these scenarios, NLP and OCR are translating everyday documents into workable data for faster processing and applying the full IA platform to generate cost optimization and improve revenue capture across a health system’s enterprise.

The Right Technology = The Right Technology Partner

By deploying an IA platform, RCM can tackle numerous challenges facing the healthcare industry. Healthcare leaders can benefit from reduced administrative errors and eliminate the interoperability challenge for data being transferred from different systems resulting in improved revenue and reduce costs.

But, Mr. Barrett provides on last bit of advice:

Given the high cost associated with developing these digital capabilities, health systems need a partner who not only offers the right model, but has made necessary investments toward cutting edge IA research, fully-staffed teams with subject matter expertise, and data-rich analytics needed to foster ongoing performance improvement. With an aligned partnership and IA platform, health systems can produce successful results that achieve intended benefits.


Indeed, healthcare systems cannot do it alone. There are a seemingly endless number of new startups popping up each week. But, partnering with the right vendor -- with years of experience and proven track record of innovation -- will be key.

OrboGraph has been a technology partner for RCM companies, clearinghouses, medical billers, medical lockbox providers, and healthcare providers for nearly a decade. OrboGraph continues to innovate, integrating its OrbNet AI technology into our OrboAccess solution suite for electronification of paper-based remittances and EOB/EOP processing and correspondence letters management.

EOB for Gif

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