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AI Innovation

Both the financial and healthcare industries are undergoing an AI evolution. Review our vision for Artificial Neural Networks (ANN) and deep learning targeting these industries.

AI and Deep Learning-Based Check Processing

Check recognition & fraud detection are the most important components in today's check processing and omni-channel capture. Learn how OrboAnywhere using OrbNet AI technology reduces costs and mitigates risk for any check image capture workflow.

AI-Based Healthcare Electronification

OrboAccess, powered by OrbNet AI, provides electronification to remits and payments, enabling RCM companies to automate posting, improve research, and deliver business intelligence

About Us

Celebrating 25 years of innovation, OrboGraph has transformed into an AI company delivering targeted automation solutions to the banking and healthcare industries.

Resources

From news and events to case studies, trends, and videos, this section provides a range of information resources for payment automation in the banking and healthcare industries.

Blogs

OrboGraph produces four blog series on a weekly basis covering topics from check processing, fraud prevention, AI technologies, RCM, and healthcare electronification. Select one the blog to the right. We hope you enjoy!

AKASA Survey: Hospital Revenue Cycle Automation Adoption Sees 12% Increase

According to an AKASA-commissioned survey that included 400 chief financial officers and revenue cycle leaders at hospitals and health systems in the United States, with responses collected between May 27 and June 28, 2021, the number of health systems using revenue cycle automation has increased by 12% since last year.

RevCycleIntelligence.com reports that the COVID pandemic brought unstable claim volumes and placed heavy workloads on revenue cycle teams, prompting more health systems to turn to revenue cycle automation to minimize costs and improve workflows.

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More than three in four (78 percent) of the health systems surveyed are currently using or are in the process of implementing revenue cycle automation, according to the survey results. That is a significant increase compared to last year’s survey which reported that 66 percent of health systems were using revenue cycle automation.

“The findings underscore that automation serves as a backbone for healthcare financial leaders looking to streamline complex staff workflows,” Malinka Walaliyadde, co-founder and chief executive officer of AKASA, stated in the press release. “The opportunity going forward for provider organizations is to expand their ambitions and scope for automation.”

The survey also found that just over 32 percent of health systems that are not using revenue cycle automation currently plan to do so in 2022. A small portion (5.41 percent) of health systems responded that automation implementation was a priority for 2021.

“Instead of identifying dozens of small, discrete use-cases and never getting past the first few due to high setup and maintenance costs, leaders should consider solutions that can be deployed rapidly with minimal disruption,” Walaliyadde added. “The goal is foundational, end-to-end automation for entire functions, driving giant leaps in efficiency.”

One takeaway from the survey is that revenue cycle automation has clearly elevated itself from an emerging trend to a necessary tool. As noted by RevCycleIntelligence:

Following the coronavirus pandemic and the financial losses that accompanied it, experts agree that workflow automation is a key strategy for revenue cycle management optimization.

In order for quick reimbursement, revenue cycle team members frequently perform repetitive tasks such as claim status follow-ups. Implementing automation technology can speed up this process and allow staff to focus on other complex tasks.

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Automation may also aid in speeding up patient collection which can be a lengthy process. Consumers do not prefer manual and paper-based transactions for patient collections either, according to a past report from InstaMed.

We've noted previously that "early adopters" as the pandemic began saw improvement nearly immediately. These early adopters are turning towards a hybrid electronification approach leveraging AI, where a multitude of different technologies and strategies -- including multiclass classification models, detection and self learning, classification, and inference -- are utilized to read and extract the data from remits and EOBs/EOPs to generate an electronic EDI 835 file, ready for autoposting into an organization's patient management, healthcare information system, or to downstream BI systems to support their interoperability initiatives.

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