Using RPA in Revenue Cycle Applications
Can RPA improve your revenue cycle? The quick answer is definitely yes, with the right technology and experienced systems integration partner.
A subset of Artificial Intelligence (AI), ReMedics uses Robotics Process Automation (RPA) in our Operations Group to create streamlined workflows where the need for human intervention is minimized and/or removed. Using RPA in Physician Practice Group revenue cycle applications, such as data verification and bank reconciliation, significantly reduces manual processes and subsequent human errors by mapping interactions between traditional data entry tasks and core information systems. RPA helps to lower administration costs and denials by identifying common errors and automating alerts for correction or missing information. Combined with machine learning capabilities, RPA programming technology can be used to enhance and streamline many of today’s revenue cycle challenges.
Benefits of RPA and Revenue Cycle Improvements
Robotics Processing Automation (RPA) provides significant opportunities for creating permanent revenue cycle improvements, including:
- Improved accuracy over redundant manual data entry
- Unattended and attended payment processing applications
- Automated repetitive tasks in conjunction with AI and machine learning
- Reduced denials by identifying common claim errors
- A more productive staff, focused on high-value work
- Ability to move data across multiple Practice Management (PM) Systems and other business applications
- Connecting disparate systems where APIs don’t exist
- Data is efficiently stored for auditing and regulatory requirements
- Automated processes can adjust and scale to changes in underlying systems
ReMedics can help your organization to explore RPA opportunities and examine all areas of the revenue cycle where repeated processes can be substantially improved. Contact ReMedics online or call 440-671-7700 to talk about how we can use advanced technology to work for your organization.
*Editor’s Note: Portions of this article were previously published by our technology partner, RCMS LLC, and used with permission.