MEDICAL DATA EXTRACTION USING MINSKY^TM AND GENERATIVE AI

Background/Problem Statement:

Typically, Medical billing is a complex and time-consuming process that involves analyzing detailed medical records and generating relevant data for filing insurance claims to process payments. The aim of this case study is to streamline healthcare medical billing using our Ai proprietary Engine Minsky^TM , Robotic Process Automation (RPA) leveraging Generative Ai / Named Entity Recognition (NER) techniques

Solution Overview:

The solution design was based on using Minsky^TM as the front end for user inputs and data extraction while using advanced NER techniques. The Solution was achieved by following these steps at a high level:

• Upload the medical document that needs relevant pre-defined data to the Minsky Database. This step is accomplished using the Minsky frontend.
• Document is then submitted using an appropriate API to the Generative Ai Engine (OpenAI) with the Tags that are to be used for data extraction.
• Corresponding Data from the Tags is then returned to Minsky (using APIs) for each Medical Record Document and also saved in the Minsky^TM DB
• RPA was then used to populate forms/bills for each Medical Record and submitted electronically to the Insurance company for payments.

Key Benefits (Minsky):

• User-Friendly cloud-based AI platform
• Scalable across various domains/data.
• Provides you a list of % dependency features that can be used to optimize the outcomes.
• Ability to fine tune or optimize the models by trying different algorithms / prediction attributes
• Easy integration with other third-party solutions such as TABLEAU for data visualization

Results of Data Extraction for Selected Tags

Results:

• Eliminate errors and manual work that are otherwise used for Medical Billing.
• Able to perform data extraction tasks much faster than manual methods for Improved productivity and efficiency.
• Seamless integration of Minsky with internal Finance systems and the Generative Ai engine
• This solution can be adapted to a wide range of data types and structures, making it suitable for diverse data extraction tasks.
• Our solution can be scaled to efficiently process large volumes of data, making it suitable forbusinesses with growing data needs
• Faster ROIs