NLP Automation Process to Reduce Medical Terminology Errors

• Manual document verification is a hard and time-consuming process due to the variety of document formats and file types.
• Document analysis is not limited to industrial sectors, a flood of documents is prepared and used be it clinical, environmental, manufacturing, or construction.
• Clinical research documentation needs to be more accurate because it is used for medical treatments, tests, and research investigations.
• For instance, creating a Protocol paper and many supporting documentations for the review board is required to obtain approval for a medical trial or study.
• 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.
• Machine learning (ML) and natural language processing (NLP) technologies are crucial for intelligent document analysis and understanding. They assist in the knowledge extraction process from unstructured data, including emails, photographs, social media posts, and written documents.
• NLP is the best option for digital transformation projects when you consider that 80% of all corporate data is apparently unstructured.
• By intelligently enhancing business processes, NLP may provide measurable benefits across industries and corporate functions, such as improving compliance, data governance, risk management, and internal operational efficiencies.

The Advantages 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 did we develop an NLP document automation process for the 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.

Technology

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.