Automation Process to Reduce Medical Terminology Errors | California

NLP Automation Process to Reduce Medical Terminology Errors– Overview

  • Due to the wide range of file types and file types, manual document authentication is a complex and time-consuming operation.
  • The compilation and utilization of documents is widespread, whether in the manufacturing, clinical, environmental, or construction sectors.
  • Because medical research documentation is utilised for medical treatments, testing, and research papers, it needs to be more accurate.
  • For instance, in having to get all the review board’s clearance for a medical trial or study, a Protocol paper and some other related documents must be produced.
  • Due to medical terms mismatch and conceptual diversity, the process of physically examining papers for quality and completeness is currently time-consuming, tedious, and possibly mistake.
  • For intelligent document analysis and understanding, machine learning (ML) and natural language recognition (NLP) technologies are essential. They help in the process of extraction of knowledge from unstructured data, such as emails, photographs, posts on social media and documents.
  • Once you considers that 80% of all corporate data is ostensibly unstructured, NLP is the finest solution for digital transformation efforts.
  • NLP may give demonstrable benefits across industries and corporate activities by intelligently boosting business processes, such as enhancing compliance, governance, regulatory compliance, and interior process efficiency.

Benefits of using NLP in Document analysis.

  • Perform extensive analysis.
  • Obtain a more accurate and unbiased analysis.
  • Reduce costs and improve operations.
  • Improve client satisfaction.
  • Motivate your staff.
  • Obtain accurate, useful insights.

How we developed NLP Document automation process for healthcare industry?

  • 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.
  • 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.




Market size: NLP document analysis

The document management systems market size reached USD 5.40 Billion in 2021 and is expected to register a revenue CAGR of 11.2% during the forecast period.

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