Like most U.S. medical centers, declining margins have posed significant threats to financial viability. Some of the financial bleeding is due to predominantly manual processes — despite the transition to electronic health records (EHR). In general, 90% of healthcare providers are heavily dependent on manual processes for claim submission and follow up. Up to 60% still rely on manual processing for claim status and payments.
The medical center used a major bank’s lockbox for financial matters related to claims, however insurance documents and other correspondence that the medical center needed to act on was routinely mis-sent to the lockbox address. The bank then had to box and ship the correspondence to the medical center daily. Once patient financial services staff received the boxes, each document had to be put through a labor-intensive, five-step manual process. With the onerous process and the two-plus week processing backlog, the hospital risked missing critical deadlines to respond to correspondence matters. The delay also resulted in more denied authorizations and denied payments.
This medical center had eight patient financial services staff members manually processing insurance correspondence — averaging resolution of 80 to 100 documents per day. It took 20 minutes to locally scan and manually index and enter each record into the medical centers’ ECM and EHR systems so it could be attached to the correct patient chart. Routinely, the medical center had a two-plus week backlog of correspondence to process — which delayed revenue to the medical center. Furthermore, the process was extremely complex. There were 200-plus document types with 1,000-plus variations that had to be routed to the appropriate queue.
The medical center’s correspondence processing is now fully automated and streamlined into a three-step electronic process. Claims are resolved much faster — each staff member can now handle 250-300 correspondence documents daily, five minutes per document — and the backlog is down to just two days. In addition, much of the complexity has been removed from the process by reducing document types from 200-plus to only 22.
The bank that houses the lockbox is now scanning and indexing documents before providing electronic files to the medical center — which has reduced local scanning and paper consumption by 90%. With automation, insurance authorizations are received more quickly by the medical center, which enables surgeries to be scheduled faster and enhances patient communication. In addition, records can be validated against the database to reduce manual entry, allowing staff to only work on exceptions rather than reading and keying everything.
The automated solution has also enabled higher auditing compliance and faster revenue generation for the medical center. And since the automated solution went live before the Covid-19 pandemic hit the U.S., medical center staff were able to connect to the solution while working remotely from home and continue processing correspondence quickly during the crisis.
Ricoh had been providing printers and Managed Print Services to the medical center for about eight years when the opportunity to provide three high-speed scanners arose. At the time, the medical center wanted to scan in the paper insurance correspondence documents on-site and use optical character recognition (OCR) data. We provided the scanners and continued to work collaboratively with the medical center staff — asking what more they would like the scanners to do?
As the relationship progressed, we developed the Ricoh Electronic Data Exchange (EDE) software platform that enables transfer of images and documents via HL7 integration to the medical center’s EHR system. The development of RICOH EDE paved the way for developing a new three-step solution for end-to-end automation of the medical center’s insurance correspondence process — Ricoh’s Correspondence Management Solution, a new offering through Ricoh Patient Information Management Services.
With the solution, insurance correspondence information is captured automatically from various document sources — bank lockbox, electronic data and scans from the bank, email, fax, etc. Each document and document format type is then classified, extracted and indexed. The indexed information is then automatically matched to the patient’s electronic health record (EHR) with machine-learning accuracy for timely response to document submissions and insurance follow-up. In the final automated step, patient information is linked to the EHR and documents are stored in the medical center’s enterprise content management (ECM) repository to optimize the work queue and expedite response time.