Powerful Insurance Claims Rejection Predictive Model

Healthcare

Powerful Insurance Claims Rejection Predictive Model

03 Engineers

08 Months

60%

Efficiency increase in accuracy of insurance claims rejection prediction

Technology

Python Django, AWS RDS, PostgreSQL, Docker

Services

Data engineering and Web development

Location

Palo Alto, California, USA

Overview

A California-based client, the creator of one of the world’s first cross-functional Healthcare revenue intelligence software, approached us to enhance their product with advanced analytics capabilities. The objective was to develop a predictive model for insurance claim rejections that would provide healthcare organizations with a means to forecast client eligibility. This would help circumvent claim denials, mitigate financial burdens, and optimize monetary compensations.

Challenges

Knowledge Gap: Implementing a predictive analysis for insurance claims required in-depth familiarity with the revenue cycle management of US healthcare.
Technical Expertise: The project required a sophisticated blend of machine learning and data engineering, which is a complex and rare combination.
Precision in Automation: The revenue intelligence software required precise and detailed modules for automating the creation, ingestion, devalidation, and monitoring of rules for prediction.

High Level Design Architecture

Solutions

Industry Expertise: Leveraging our extensive experience with healthcare-related projects, we approached the task of enhancing claim acceptance and rejection prediction.
Rule-Defining Module: We created a module defining prediction rules based on a thorough analysis of insurance claims data. Alternate suggestions were provided for every predicted claim denial.
Claims Verification and Workflow Automation: We integrated modules that automated the verification of claims and the overall workflow process.
Devalidator Module: A unique devalidator module was developed that eliminated irrelevant rules and allowed new ones to be incorporated in predictive modeling.

Result

Our team’s extensive knowledge in machine learning and predictive modeling led to successfully enhancing the client’s existing models. The implemented modules allowed healthcare professionals to predict claim rejections accurately, improving patient engagement, decreasing cash flow cycles, and increasing revenues. In response to the evolving nature of the healthcare sector, our team has remained engaged in regular product updates based on fresh modifications and specifications.