PHARMACY CLAIMS

Use of Artificial Intelligence in Determining/Predicting
Eligibility of Pharmaceutical Claims

Background/Problem Statement:

The US federal government has several Pharmacy reimbursement programs that require drug manufacturers to provide outpatient drugs to eligible health care organizations and covered entities at significantly reduced prices. These programs cover entities to stretch scarce federal resources as far as possible, reaching more eligible patients and providing more comprehensive services. These Govt. Pharmacy programs were established in order to provide discounted drugs for covered entities, such as “high-Medicaid public and private non-profit hospitals, community health centres’, and other safety net provider to help those facilities to deliver pharmacy services to those underinsured or uninsured outpatient populations Each of these processes comprise of several activities/forms and lot of manual work is involved that are time consuming but critical for to reduce costs to Insurance companies. The objective of the project was to decrease insurance reimbursement and high levels of uninsured patients and healthcare providers are required to be more cost effective in delivering their services. Therefore, after a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to predict the claim eligibility for these programs for individual patient claims.

Solution Overview:

After a comprehensive evaluation of the client‘s challenges/data, we used Minsky to accurately model the historical data which comprised patient related claims data such as Patient ID, Patient DOB, Pharmacy ID, Pharmacy Name, Prescriber ID, Prescriber Address, Drug Name, Patient Paid Amount, Total Amount Paid, Total Claim amount etc. Once the detailed modelling was performed by Minsky for the selected Algorithms, historical data used by Minsky was separately analysed to predict the claim eligibility for a specific Govt Pharmacy program based on real time data for individual patient Claims.

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

Project Results:

For the above project data we used two Ai techniques for getting better model accuracy.
i. Reducing/dropping the null values.
ii. Filling the nullified values.
Based on the data, our analysis shows that ADA Boost Classifier (99%) gave the highest model accuracy for both techniques. In terms of the attributes data, for both (i), (ii) Ai techniques Pharmacy Dispensary fees information played a major role and showed the highest dependency cause for the eligibility criteria for a specific Govt. pharmacy program followed by Cost Savings information

Need For Solution:

This Ai powered project enabled our client to find out three things in their existing process
i. To improve the chances of eligibility claims
ii. To predict which claim will be ineligible
iii. For the ineligible claims, to find which feature importance attributes from Minsky models to fine tune /improve the claims prior to submission so as to improve the chances of eligibility.

Benefits:

  • Improved process efficiency.
  • Optimized Claims Management for Reduced Costs and Operational Risks.
  • Faster and Reliable service
  • Better employee time utilization.
  • AI diagnosis model can diagnose hydrocephalus by reading brain CT images. It achieves this function by analyzing the factors of the shape and volume of ventricle, Evan index and age, which is a new method for diagnosing hydrocephalu
  • AI diagnosis model can diagnose hydrocephalus by reading brain CT images. It achieves this function by analyzing the factors of the shape and volume of ventricle, Evan index and age, which is a new method for diagnosing hydrocephalu
  • AI diagnosis model can diagnose hydrocephalus by reading brain CT images. It achieves this function by analyzing the factors of the shape and volume of ventricle, Evan index and age, which is a new method for diagnosing hydrocephalus
  • AI diagnosis model can diagnose hydrocephalus by reading brain CT images. It achieves this function by analyzing the factors of the shape and volume of ventricle, Evan index and age, which is a new

Ready to get started? lt's fast, free and very easy!

Copyright © 2024 Ai Labs

Terms of Use  |  Privacy Policy