Enhance medical accuracy with text analytics
Business challenge
Time constraints: Manually reviewing and verifying large amounts of medical documents can be time-consuming and may delay the completion of the research project.
Human error: The manual verification process is prone to errors, such as missed or misinterpreted information, which can affect the accuracy and integrity of the research data.
Cost: Manually reviewing and verifying large amounts of medical documents can be costly in terms of labor and resources.
Lack of standardization: Different medical documents may use different terminology, making it difficult for the team to consistently understand and verify the information.
Data privacy and security: The manual verification process may increase the risk of data breaches and violations of patient privacy.
Solution overview
Document analysis is not limited to specific industries; a large number of documents are created and used in various fields such as clinical research, environmental studies, manufacturing, and construction.
In clinical research, documents must be highly accurate as they may be used for medical treatments, tests, and research trials.
For example, obtaining approval for a medical trial or study involves preparing a protocol document and various supporting documents for review by a board.
OptiSol partnered with a clinical research company to develop an NLP-based document analysis solution that can verify multiple documents quickly and with high accuracy.
The solution includes a custom NLP pipeline to parse different documents used in a study, such as protocol documents and consent forms, and follows a universal structure to ease interpretation by NLP packages.
The system extracts relevant sections of different documents, such as the adverse symptoms section of a protocol document and a consent form, and performs syntactic and semantic analysis to compare them and report if they match, and if they have the same entities, nouns and verb phrases.
Business impact
Improved decision-making: Provides insight into historical sales patterns and trends, which can be used to make informed decisions about product development, resource allocation, and future growth.
Improved accuracy: Improves the accuracy of the verification process by reducing human error and standardizes the interpretation of medical terminologies.
Reduced costs: Reduces costs by minimizing the need for manual labor and minimizing the time required to complete the verification process.
Scalability: More scalable than manual verification, making it easier to handle a large number of documents or research studies.
Improved data quality: Ensures the quality and consistency of data, which is crucial for accurate analysis and decision-making.
Speed up the process: Speeds up the process of verification and reduces the time needed to complete the research, which can lead to faster time to market for the results.
