GIGI is a revolutionary, data-driven algorithm supported by machine learning designed to predict the likelihood of a claim’s certification with a 92% accuracy level. GIGI can eliminate 90% of the labor costs associated within the certification process while significantly increasing the accuracy of the first level triage in the UR review.
GIGI may be accessed as a SaaS via API and integrated within a client’s existing claims management system, where the client’s own business rules can be applied. Alternatively, clients may use EHS’s BaseLine Platform.
In 2019, EHS leadership committed to create a third generation medical triage technology. The vision was to create an AI supported, data-driven medical authorization algorithm to eliminate many of the issues incumbent in the current system and further reduce the need for human intervention in the process. Having spent a year visiting with clients about the potential of the concept, we developed an algorithm that would predict the likelihood of a medical request being certified via the UR process. Our goal being to eliminate the time, expense and errors associated with the current triage processes for medical request authorization.
The predictive algorithm was developed using a logistic regression model to predict the likelihood of certification. The model was deliberately constructed using only those variables that would be known to the user when the treatment request is first received. The analysis was first performed on a split sample; that is, the model was built on a random half subset of the data and then the parameter estimates from the first subset were used to score the holdout data set. Subsequently, the model was rerun on the full data set to create the predictive weights for the algorithm.
GIGI has a demonstrated 92.5% accuracy level in identifying medical request certifications if sent to UR.
GIGI can also relay the stated Medicare Fee Schedule cost of the requested product/procedure by patient zip code.
GIGI can reduce 90% of the labor costs associated within the certification process while significantly increasing the accuracy of the first level triage in the UR review.
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