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RGERIA's proprietary AI algorithms employ a combination of supervised and unsupervised machine learning techniques to formulate highly predictive models.
These techniques enable the algorithms to handle both quantitative and qualitative data effectively, which is vital for the complex and multifaceted nature of medical claims processing as well as other similarly complex healthcare processes.
Specifically, to ensure unparalleled accuracy and uphold patient privacy, ClaimGuard's algorithms and models utilize:
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A proprietary process for de-identification and re-identification that adheres to HIPAA's 'Safe Harbor' guidelines
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Both historical and real-time synthetic claim data unique to the healthcare provider, de-identified to ensure privacy, on the quantitative side
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Real time access to payer contracts, both in-network and out-of-network, along with claim forms, Explanation of Benefits (EOBs), and doctor’s notes on the qualitative side
Once the models are trained:
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Each patient's de-identified data is securely transmitted to a designated cloud storage location where ClaimGuard's models conduct a comprehensive analysis against millions of potential outcomes to assess the probability of a medical claim denial
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ClaimGuard's sophisticated AI algorithms then recommend detailed remediation actions to significantly reduce the risk of medical claim rejections
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