Customer: Health information management solution
Challenge: Process semi-structured and unstructured medical request forms faster
Solution: Intelligent document processing algorithms to read, extract, and organize unstructured and narrative text
Medical release documents and forms are highly variable, as each institution creates their own version (some even hand-written if it’s a small provider.) Our customer, a health information management company, processes forms and relays necessary information to other healthcare parties involved such as hospitals and doctors’ offices.
Traditionally, the process involves employees manually scanning documents to find the information of interest and entering it into their systems of record. This is a highly repetitive and inefficient process. These documents can be either digital PDFs or scans of paper forms which are difficult to parse with an off-the-shelf document processing solution.
Starting with our pre-built suite of text and document technologies, Infinia ML developed a customized machine learning approach to extract sensitive information from our customer’s semi-structured forms. This process involved first categorizing the forms based on the different elements included in the document. From there, we deployed a combination of optical character recognition (OCR), name-entity recognition (NER), and natural language processing (NLP) to automate the digitization and extraction of the information of interest.
By cutting out the pain point of manually reading and entering individual requests, our customer can now more efficiently use their resources to process more medical release requests. Infinia ML’s solution allows the company to rapidly and accurately read, extract, and organize data to be shared with other healthcare stakeholders.
Infinia ML developed a solution to yield savings of $1.6M over three years with the power of automation.
Infinia built complex document processing capabilities to unlock almost $900K in annual customer savings.
Infinia ML developed machine learning models to extract text data from non-standardized documents.
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