Our AI Tech – Anthro Innovations

Our AI increases efficiencies, reduce stress, improve revenue, and lower costs.

The best and most effective AI solutions are meant to work with staff to get tasks completed more efficiently and accurately freeing staff time for more profitable tasks.

HOW IT WORKS

B.R.U.C.E., our AI, developed by our team of research scientists, engineers and healthcare professionals, is a suite of AI tools designed to provide a deep learning, artificial intelligence system which understands and makes decisions on insurance claims. Our state-of-the-art machine learning works with your legacy systems and databases.
B.R.U.C.E. learns with every experience, meaning it learns like a human, getting more accurate and making more nuanced decisions over time. Each component of our AI reinforces the next, creating a continuous learning loop. Our insurance AI revolves around 4 key factors – contextual understanding, extensive knowledge bases, timely adjudication, and claims & medical records

UNDERSTANDING

B.R.U.C.E. cognitively understands and learns from your claims, policies, and medical records. B.R.U.C.E. utilizes this cognitive understanding to extract all the rules relating to claims saving significant time, human resources, and money.

KNOWLEDGE BASES

Our unique knowledge bases contribute to the success of B.R.U.C.E.. They include medical knowledge encompassing the definitions, characteristics and relationships of certain diseases, symptoms, and treatments. Our insurance billing knowledge base contains the expertise accumulated from our 45 years of billing experience, AMA guidelines, and other insurance rules. We use the knowledge from our continuously updating digital encyclopedias, combined with all the data from your policies, claims and when appropriate, medical records to understand the nature of the claim.

ADJUDICATION

Human adjudication or Anthro Innovation adjudication – what’s the difference?
Anthro Innovation’s AI works like a human, only faster and more efficiently. B.R.U.C.E. combines our knowledge bases, understanding of the specific policy rules, claim details, and supporting records, where applicable, to determine if a claim should be paid. We reduce human intervention for all but the most ambiguous claims. Our AI allows claim adjudication in minutes rather than days.

Connecting to your Legacy System – Claims and Medical Records

Our team is experienced in connecting to your legacy systems with APIs and HL7 standards in a fully compliant, HIPPA environment.

Our AI Software Leverages Your Resources

PDFs, faxes, websites, and email are difficult for most software to read, let alone understand because it is unstructured data. For example, we can take a PDF (or scanned image) as input, and train our context-specific OCR and natural language models to empower our software robots to read and interpret what’s on each page and turn the key elements into an actionable format.
B.R.U.C.E. understands insurance claims including medical claims by combining natural language processing, machine learning, our unique knowledge bases, and extensive algorithms. Our ability to understand your documents in any digital format and our customized interface provides you with all your data at your fingertips. Your insurance policies and documents are read and understood, the claims are analyzed, and determination made immediately if a claim s should be paid. Rapid adjudication at a fraction of the time and cost, reliable and repeatable with B.R.U.C.E.

AI You Can Trust

All AI machine learning applications need to maintain constant vigilance on the tradeoff between recall (the fraction of decisions that are automated) and precision (the fraction of those automated decisions that are correct). Anthro Innovations specializes in applications where high precision is important. We target human-level, or better, precision as our baseline. This means you can trust the solutions we deliver.

We work with partners from insurance companies, TPAs and medical practices tailoring our platform to meet their specific needs.

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