US-20260127214-A1 - AUTOMATIC FAX DOCUMENTS PROCESSING SYSTEM USING INTEGRATED PROGRAMMATIC AND SPECIALIZED GUIDED AND CONSTRAINED ARTIFICIAL INTELLIGENCE

US20260127214A1US 20260127214 A1US20260127214 A1US 20260127214A1US-20260127214-A1

Abstract

An automatic fax document processing method is disclosed which receives one or more fax documents on an online healthcare platform from a plurality of sources, including, but not limited to, insurance companies, and one more medical service providers. The received fax documents are classified using integrated programmatic and specially guided and constrained artificial intelligence based on predefined categories, including authorization, denial, referral, payment, and miscellaneous. The relevant information from the classified fax documents is extracted using machine learning techniques and based on the extracted information an action is triggered. A final document is generated that is approved and saved by the expert user.

Inventors

Kushagra Mittal
Caleb Allen
Manish Shukla

Assignees

Ocean Friends, Inc.

Dates

Publication Date: 20260507
Application Date: 20251106

Claims (15)

1 . A method of automatically processing at least one fax documents received from at least one sources in an online document management platform, the method comprises: executing code using at least one processor of a computer system to cause the computer system to perform operations comprising: receiving the at least one fax documents from the at least one sources; classifying the received at least one fax documents into predefined categories; utilizing a trained machine learning model to automatically extract relevant information from the at least one classified fax documents, wherein the machine learning model is guided and constrained by a prompt that references historical classification data to improve output results, specifies classification categories, maps data in the classified fax documents to the classification categories, extracts the classified data, and trains the machine learning model to improve for future classification; performing actions based on the extracted information; and approving and saving a final document prepared automatically by extracting the relevant details from the at least one classified fax documents.
2 . The method of claim wherein the classification of the at least one fax documents into predefined categories is performed automatically using a machine-learning model trained on a dataset.
3 . The method of claim 1 wherein the classification of the at least one fax documents is performed based on keywords, content, and semantic search.
4 . The method of claim 1 wherein the classification of the at least one fax documents further comprises: categorizing the received fax documents into relevant and irrelevant based on the content; tagging the categorized fax documents into additional categories, including authorization, denial, referral, payment, and miscellaneous; and extracting the relevant information from the categorized fax documents.
5 . The method of claim 1 wherein the extraction of relevant information further comprises: extracting patient information, insurance coverage details, and authorization status for authorization documents; extracting referral details, including referring letters from experts and recommended treatment for referral documents; extracting payment amount, due dates, payment method, and payer information for payment documents; and extracting reasons for the denial and related claims information for denial documents.
6 . The method of claim 1 wherein the actions include processing payments, sending notifications to parents for authorization, scheduling follow-up consultations, providing updates to insurance providers, and escalating denied claims for further review based on the extracted information.
7 . The method of claim 1 wherein the machine learning model generates a confidence score to check the accuracy level of the extracted information.
8 . A system to automatically process at least one fax documents received from at least one sources in an online healthcare platform comprises: one or more processors; one or more databases, operatively coupled to the one or more processors that when executed cause the one or more processors to perform operations comprising: receiving the one or more fax documents from the one or more sources on the online healthcare platform, wherein the one or more sources include insurance companies, one or more experts treating a user, and so on; classifying the received one or more fax documents into predefined categories using a classifier; automatically extracting relevant information from the one or more classified fax documents utilizing a trained machine learning model to extract the relevant information using an extractor, wherein utilizing a trained machine learning model to extract the relevant information using an extractor comprises utilizing the trained machine learning model to automatically extract relevant information from the at least one classified fax documents, wherein the machine learning model is guided and constrained by a prompt that references historical classification data to improve output results, specifies classification categories, maps data in the classified fax documents to the classification categories, extracts the classified data, and trains the machine learning model to improve for future classification; performing actions based on the extracted information using an action module, wherein the actions are triggered as per the requirement; and approving and saving a final document prepared automatically by extracting the relevant details from the one or more classified fax documents using a document generator, wherein the final document can be edited or modified by the therapist, if necessary.
9 . The system of claim 8 wherein the received one or more fax documents and the final document is accessible to the user via, a user interface integrated within the online healthcare platform.
10 . The system of claim 8 wherein the classifier further classifies the fax document into different chunks, including relevant chunks, and irrelevant chunks.
11 . The classifier of claim 10 wherein the relevant chunks include relevant pages of the fax document, and the irrelevant chunks include irrelevant pages of the fax document.
12 . The system of claim 8 wherein the one or more databases store training datasets and historical fax documents, enabling the machine learning model to continuously improve classification and extraction accuracy by updating with new data.
13 . The system of claim 8 wherein the machine learning model generates a confidence score to check the accuracy level of the extracted information.
14 . The system of claim 8 wherein the users are allowed to create and personalize the fax documents template, thereby enhancing flexibility and personalization in communication.
15 . The system of claim 8 wherein the predefined categories include, authorization, denial, referral, payment, and miscellaneous.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) This application claims the benefit under 35 U.S.C. § 119(e) and 37 C.F.R. § 1.78 of U.S. Provisional Application No. 63/716,727, which is incorporated by reference in its entirety. FIELD OF THE INVENTION The present invention generally relates to the field of electronics, and more specifically to an automatic fax document processing system that receives one or more fax documents in an online healthcare platform from a plurality of sources, including insurance companies, experts, and so on, to classify and extract relevant information from the fax documents. BACKGROUND OF THE INVENTION Managing a large volume of documents and extracting relevant information efficiently has become a significant challenge in the modern healthcare industry. Healthcare providers deal with numerous types of documents daily, such as medical records, insurance authorizations, referrals, and billing information. Traditionally, these documents were processed manually, which was time-consuming and prone to human errors. Manual data entry and information extraction often led to delays in patient care, inefficient workflows, and difficulties in maintaining accurate records. As healthcare systems grew and regulations became stricter, the need for a more reorganized approach to document management became increasingly essential. Healthcare providers realized that relying on outdated methods for handling documentation could result in lost or mismanaged information, ultimately affecting patient care. Furthermore, as the amount of paperwork increased, so did the operational costs associated with processing these documents. Many organizations faced challenges in keeping up with the paperwork while ensuring that they complied with all regulations and maintained high-quality care for their patients. To address these challenges, many healthcare providers began exploring technological solutions to improve the way they handled documents. The rise of digital technologies offered new opportunities to automate and optimize various processes within healthcare systems. For instance, the start of cloud storage enabled healthcare providers to store and manage documents more effectively, allowing for easier access to critical information. However, simply digitizing documents was not enough. SUMMARY An automatic fax document processing method is disclosed which receives one or more fax documents on an online healthcare platform from a plurality of sources, including, but not limited to, insurance companies, one or more experts treating a user, and so on. The received fax documents are classified based on predefined categories, including authorization, denial, referral, payment, and miscellaneous. The relevant information from the classified fax documents is extracted using machine learning techniques and based on the extracted information an action is triggered. Finally, a final document is generated that is approved and saved by an expert user such as a therapist. The therapist can make any changes in the final document, if necessary. In an embodiment of the present disclosure, an automatic fax document processing system is disclosed which receives one or more fax documents on an online healthcare platform from a plurality of sources, including, but not limited to, insurance companies, one or more experts treating a user, and so on. The online healthcare platform is operatively coupled with a document analyzer. A classifier, integrated within the document analyzer classifies the fax documents into predefined categories, including authorization, denial, referral, payment, and miscellaneous. An extractor extracts the relevant information from the classified fax documents by using a machine learning model. An action module performs the actions based on the extracted information. Finally, a document generator generates a final document that is approved and saved by the therapist. The therapist can make any changes in the final document, if necessary. Furthermore, in at least one embodiment, the actions include processing payments, sending notifications to parents for authorization, denial, or rejection, scheduling follow-up consultations, providing updates to insurance providers, or escalating denied claims for further review or any other relevant actions based on the extracted information. Additionally, in at least one embodiment, the machine learning model generates a confidence score to check the accuracy level of the extracted information. BRIEF DESCRIPTION OF THE DRAWINGS The systems and methods described herein may be better understood, and their numerous objects, features, and advantages are made apparent to those skilled in the art by referencing exemplary embodiments depicted in the accompanying figures. The use of the same reference number throughout the several figures designates a like or similar element. FIG. 1 depicts an exemplary automatic fax document processing system. FIG. 2 depicts an exemplary auto