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Smarter Healthcare claims processing with Machine Learning

Smarter Healthcare claims processing with machine learning

Most healthcare insurers employ allied health professionals for back office functions such as claim pre-authorization and adjudication. However, staffing is problematic as these professionals’ first preference would be to work in a clinical setting, as these provide stepping-stones for overseas career opportunities. Not surprisingly, healthcare back office operations often struggle with high employee turnover, and the corresponding productivity loss, processing delays, and customer service issues.

Challenges : Improve the turnaround time of claims processing to realize prompt-payment discounts.

High attrition of medical allied professionals

Delayed claims processing

Prompt-payment discounts offered by service providers are forfeited

Solution : Auto-adjudicate claims with machine-learning algorithms

MediLink enhanced business rules with machine learning algorithms to auto-adjudicate claims based on consistency of patterns recognized from past experience.

Incorporated statistical rules and limits to flag suspected fraud;

Employed supervised learning from three years of data to label incoming claims as “Approved” or “Referred” for human verification;

Continually improved machine learning performance by incrementing training data with correct labels.

Impact : Significantly shortened turnaround time, big savings in processing cost and increase in realized discount

55-60% of claims are auto-approved in the initial trial with 95% precision rate

Millions realized in discounts for timely payment of claims

Case Study

Smarter Healthcare claims processing with Machine Learning