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

To illustrate how the IA platform truly works, let’s look at a common workflow: correspondence management. Paper document processing is still highly prevalent in healthcare, requiring significant resources from health systems. For example, issuing correct billing correspondence to patients requires receiving paper correspondence from banks, such as letters, checks, and EOBs, reviewing what is typically tens of thousands of files per day, and manually entering data from these files into subsequent workflow solutions.

With an IA delivery platform, this process can be automated by utilizing RPA to retrieve these documents; OCR and NLP technology to convert these documents into standard file formats; and then, once again, RPA to process and attach necessary documents into patients’ accounts in the accounting or indexing system. While these activities are taking place, the integrated workflow platform is tracking the activity and flagging any exceptions or high-risk materials that need to be pulled out and handled by humans.

An IA delivery platform also gives health systems access to better decision-making tools since the technology can consume large volumes of data and subsequently create learning algorithms that make consistent decisions on behalf of operators based on the task at hand. For example, ML can apply historical claim reimbursement trends when assessing data to predict potential write-offs; then use the integrated workflow platform to either escalate high priority items to operators or direct low-dollar write-offs to RPA to process. These learning algorithms can be applied to many situations within the revenue cycle to achieve greater cost optimization and streamline revenue cycle operations.