Automated claim risk factor identification and mitigation system

Abstract

A system to predict and identify claims that have a high likelihood of exceeding a predetermined limitation in a given excess workers' compensation insurance policy and to present the automated indication of possible intervention strategies to mitigate potential claims costs. The processing system includes a computer server, database engine, computer programming instructions, network connectivity, associated claims, payment, medical, pharmacy and other relevant data, a plurality of statistical and machine learning algorithms and a method for electronically displaying and attaching the results to a business process. The system will use all available data to analyze the medical treatment pattern of a claimant and based on automated findings make recommendations as to appropriate interventions to positively impact claims costs.

Description

BACKGROUND[0001]Aspects of this invention relate to a computerized assessment and recommended intervention for high risk workers' compensation claims and, more specifically, to applying machine learning technologies to data and delivering results via electronic means over a computer network in an effort to identify and mitigate costs associated with high risk workers' compensation claims.[0002]The American workers' compensation system is a no-fault scheme that has been implemented by each of the fifty states. While each state's law may vary from that of another state, all states share the same basic concepts: (1) if an employee is injured at work, the employer must provide defined benefits (medical, indemnity (lost wages) and disability) regardless of fault and (2) the employer is immune from a tort lawsuit by the employee on account of the employee's work injury. Generally, most states allow employers to insure their obligations to provide workers' compensation benefits through, fo...

AI Technical Summary

Benefits of technology

The present invention allows for quick and accurate prediction of claim outcomes using statistical and machine learning techniques. It also includes a fully integrated scoring engine that automatically generates predictions, stores them, validates model performance, allows for model retraining, and delivers the results electronically. This invention facilitates targeted interventions based on the predictions to reduce the risk of migratory claims.

Problems solved by technology

As medical and pharmaceutical technology has advanced, so have the expenses associated with these treatments.

In addition, medical cost inflation has also aggressively trended upward over the last several decades.

Not unexpectedly, in the last decade, the primary expense associated with workers' compensation claims has dramatically shifted from lost wages to medical and pharmaceutical related expenses.

Indemnity claims involve injuries that cause the employee to be out of work for a period of time.

Some injuries are catastrophic and are known to be high risk at claim outset.

A migratory claim appears to be much like a normal risk claim and then medical conditions gradually worsen over time.

For example, initially, a low back strain may result in lost time, limited medical treatment, and dispensing of pharmaceuticals.

However, the normal claim can migrate to high risk if the claimant continues to experience pain and opts for a costly surgical intervention.

Without a different medical treatment protocol, this pattern will repeat over and over incurring hundreds of thousands even millions of dollars in costs.

The workers' compensation insurance industry and, in particular, self-insured employers and their excess carriers, have been slow to adopt automation.

Further, conventional approaches lack the ability to effectively predict migratory claims.

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