Document Automation for EOP/EOB Reconciliation
When it comes to processing provider receivables, the challenge is enormous.
Payment data is complex and can span multiple pages of a document leading to a slow, cumbersome intake and reconciliation process.
Parascript is the leader in payment automation technology for banks worldwide and this same expertise is extended into automation of the complete range of documents involved in revenue cycle management.
The challenges of claims processing, in the era of machine learning, seem like they should be a problem solved long ago.
While over 90% of claims are handled through auto-adjudication, costs continue to rise due to the need to handle non-standardized claims such as those sent in via forms (often faxed) and claims that require submission of additional supporting documentation. Both require a different approach using advanced computer vision along with machine learning.
Medical Record Retrieval & Processing
While most medical records use structured databases, the richest source of patient data is still stored within unstructured documents. The ability to efficiently access and use this data is costly.
Parascript records extraction, powered by deep learning neural networks, enables insurers and services providers to reliably create individual, indexed records from a single PDF submission. Using NLP-based techniques, the software analyzes the text of the file and identifies each document type, and separates it from the file.
Once the records are separated, additional text parsing is employed to locate data for an efficient review. The result is a more efficient and reliable review process.
Forms still start over 90% of business processes and healthcare-related processes are no exception.
Whether forms are related to patient in-take or assessment or for handling requests for prior authorization, form-based data represents a critical, yet time consuming part of a process.