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NLP for document integrity in medical records: a nurse records patient data

AI Solution

industry

NLP for Document Integrity

Challenge

Iodine Software is a software group based in Austin Texas which provides Clinical Document Integrity (CDI) software to hospital systems and clinics across the U.S. They sought to improve their CDI product by using machine learning to extract specific and relevant information from documents and build a competitive moat by leveraging their troves of valuable un-tapped data.

Solution

Working in tandem with Iodine’s existing ML team, KUNGFU.AI leveraged massively pre-trained state of the art natural language models to automatically categorize medical documentation into insurance coding categories. The performance of this model rivals human SME capability.

Outcome

With the improvements made to Iodine’s product, hospitals and clinics across the U.S. are able to recoup millions in unbilled revenue and reduce human tedium. KUNGFU.AI and Iodine Software continue to work together, identifying additional areas of ML implementation across the organization.

NLP

NLP for Document Integrity

Challenge

Iodine Software is a software group based in Austin Texas which provides Clinical Document Integrity (CDI) software to hospital systems and clinics across the U.S. They sought to improve their CDI product by using machine learning to extract specific and relevant information from documents and build a competitive moat by leveraging their troves of valuable un-tapped data.

Solution

Working in tandem with Iodine’s existing ML team, KUNGFU.AI leveraged massively pre-trained state of the art natural language models to automatically categorize medical documentation into insurance coding categories. The performance of this model rivals human SME capability.

Outcome

With the improvements made to Iodine’s product, hospitals and clinics across the U.S. are able to recoup millions in unbilled revenue and reduce human tedium. KUNGFU.AI and Iodine Software continue to work together, identifying additional areas of ML implementation across the organization.

NLP

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