New Standard for B&W Claims Capture
Using advanced machine learning, Parascript offers virtual drop-out for claims processing that has set a new standard for classification and recognition of black-and-white claims, overcoming image quality and scaling challenges. Parascript deep learning algorithms improve out-of-the-box accuracy to the industry’s highest level of over five times compared to other solutions.
Service providers expand their data capture offerings while insurers streamline their workflow processes and lower costs.
Parascript supports interactive onboarding and servicing by accurately extracting quality data from consumer documentation submitted for opening an account via a smartphone or through any other channel using Parascript mobile capture SDKs coupled with FormXtra.AI. In the background, Parascript software provides sophisticated document analysis. It automatically classifies, sorts, extracts and validates the necessary information to reduce data input requirements, fraud and “transaction friction.”
Explanation of Benefits Data Extraction | Capture
Locate and extract common “header” data including the date of notice, patient number, claim number and patient name with our automated template-less, machine learning solution. Parascript also supports claims data including service date and financial data (billed, allowed, Deductible, copay, code and paid amount). It validates the individual service lines and the claim totals to ensure that the amounts match.
Classification Made Easy
Parascript FormXtra.AI makes understanding and organizing your healthcare claims, EOBs, invoices, superbills and other documents a simple process. Even if you don’t know anything about your volumes of documents, you can automatically organize them using advanced document clustering. Once they are classified, you can create simple workflows for metadata and even use the data within your documents to ensure more-descriptive searchable data.
FormXtra.AI leverages many types of machine learning classifiers including deep learning algorithms to auto-analyze text-based documents, image-based documents or a combination of both with Content Classifiers, Visual Classifiers and Combined Classifiers. Content classification employs text-based features of documents. Visual classification uses “feature extraction” based upon the documents’ key visual characteristics without using OCR. Combined classification uses both and synthesizes the results for the best answer.
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