Challenges
- Variations in layout and organization make it difficult to integrate data from various blood report formats, which makes data aggregation across several sources more difficult.
- Manually extracting data from blood reports takes a lot of time and is subject to mistakes, which increases the chance of transcription or data entry errors.
- Lack of access to historical health data makes it more difficult to make educated treatment decisions, which delays diagnosis and care continuity.
- Paper-based report management negatively impacts healthcare workflows and patient safety, which is inefficient and raises the possibility of record loss or misplacement.****
Requirement
- Provide a mechanism for analyzing blood reports effectively.
- Ensure data accuracy by using reliable extraction techniques.
- Create an intuitive online interface to facilitatedata visualization.
- Switch to an electronic report management system for better accessibility.
Solutions Provided
- Developed a Python backend to extract raw data from PDF and picture files using FastAPI and OpenCV-python.
- AI algorithms were implemented to provide a unified representation for the gathered data.**AI algorithms**performed a significant portion of the evaluation of the acquired text data. The data is standardized, cleaned, and organized by these algorithms into a unified format that makes it simple to analyze the data for different kinds of reports.
- Our ReactJS developers crafted an intuitive web interface for seamless doctor-patient interaction using ReactJS and Material UI.
- MongoDB is used as a database to handle and keep track of patient information. The selection of the NoSQL database MongoDB was based on its flexibility in managing and archiving the diverse array of data collected from blood reports.
- This data may include historical data that can be used for trend analysis and extracted test results.
