Document analysis solution using NLP

How AI increases accuracy of medical documents verification ?

  • AI can be used to verify Medical Documents Analysis with high accuracy through a process called Optical Character Recognition (OCR).
  • OCR software uses machine learning algorithms to analyze and interpret the text in an image, such as a scanned document, and convert it into machine-readable text.
  • This text can then be compared to a database of correct information to verify its accuracy. Additionally, AI can be used to extract specific data from the document, such as patient information or diagnostic codes, which can also be used to verify the document’s accuracy.
  • Another way AI is used to verify the medical document is by using Natural language processing (NLP) to understand the context and intent of the document and flag any inconsistencies.

How our document analysis solution helps clinical research companies to minimize errors ?

Business Impact

  • Manual document verification is a time-consuming and tedious process as documents come in various formats and file types.
  • Document analysis is not limited to particular industrial sectors, a flood of documents is prepared and used be it clinical, environmental, manufacturing, or construction.
  • As in Clinical research, documentations need to be more accurate as it can be used for medical treatments, tests, and research trials.
  • For instance, getting approval for Medical Trial or Study involves the preparation of a Protocol document and varied supporting documents for the review board.
  • Currently, reviewing documents for accuracy and completeness is a manual, time-consuming, and potentially error-prone process due to medical terminologies mismatch and conceptual variance
  • OptiSol teamed up with the clinical research company in developing an NLP-based Document analysis solution that can verify multiple documents with accuracy and faster verification.

Solution Overview

  • Built a custom NLP pipeline to parse different documents involved in a study like Protocol documents, Consent forms, etc.
  • Defined and followed a universal structure for documents to ease interpretation by NLP packages.
  • Designed a dashboard to upload the documents for the automated compare and review the results.
  • Extracted relevant sections of the different documents. Example: adverse symptoms section of protocol document and that of consent form, then performed syntactic and semantic analysis to compare them and reported if they match and if they have same entities, noun and verb phrases.

Technology Stack


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