AI Powered Clinical Document Analysis for Healthcare Industry

Business Challenge

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Time constraints: Manually reviewing and verifying large amounts of medical documents can be time-consuming and may delay the completion of the research project.

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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.

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Cost: Manually reviewing and verifying large amounts of medical documents can be costly in terms of labor and resources.

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Lack of standardization: Different medical documents may use different terminology, making it difficult for the team to consistently understand and verify the information.

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Data privacy and security: The manual verification process may increase the risk of data breaches and violations of patient privacy.

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Lack of scalability: Manually verifying multiple documents with medical terminologies may not be scalable as the amount of data increases.

Solution Overview

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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.

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In clinical research, documents must be highly accurate as they may be used for medical treatments, tests, and research trials.

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For example, obtaining approval for a medical trial or study involves preparing a protocol document and various supporting documents for review by a board.

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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.

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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.

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A dashboard was designed to upload documents for automated comparison and review of results.

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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

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Increased efficiency: Increases efficiency by reducing the time and resources required to manually verify documents.

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Improved accuracy: Improves the accuracy of the verification process by reducing human error and standardizes the interpretation of medical terminologies.

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Reduced costs: Reduces costs by minimizing the need for manual labor and minimizing the time required to complete the verification process.

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Scalability: More scalable than manual verification, making it easier to handle a large number of documents or research studies.

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Improved data quality: Ensures the quality and consistency of data, which is crucial for accurate analysis and decision-making.

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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.

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Increased productivity: Allows researchers to focus on more important tasks, such as data analysis, which can lead to increased productivity.

Technology Stack

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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