Case Studies

How a company processes medical release forms faster

Estimated reading time: 2 minutes

Client: 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


Manually scanning documents for data entry

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 client, 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.


Extracting sensitive information from semi-structured forms

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 client’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.


Rapidly and accurately read, extract, and organize data

By cutting out the pain point of manually reading and entering individual requests, our client 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.

Applying these techniques elsewhere

  • Analyze large data sets
  • Parse form data into a readable format
  • Filter fields based on stakeholder preferences

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