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High-throughput, Automated Extraction of DNA and RNA from Clinical Samples using TruTip Technology on Common Liquid Handling Robots

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Robotic Process Automation Based Data Extraction from Handwritten Medical Forms.

Norbert Gal-Nadasan¹, Vasile Stoicu-Tivadar¹, Emanuela Gal-Nadasan²

¹Politehnica University of Timisoara, Department of Automation and Applied Informatics, Faculty of Automation and Computers, Romania.

Studies in Health Technology and Informatics

|October 23, 2023

Summary

This summary is machine-generated.

This study introduces a robotic process automation (RPA) robot to digitalize and extract data from handwritten medical forms. The system uses Google Cloud Vision OCR and UiPath ML for accurate data extraction and organization.

Keywords:

Optical character recognition (OCR)Robotic Process Automation (RPA)data extraction handwriting

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Area of Science:

Medical Informatics
Automation Technology
Data Science

Background:

Handwritten medical forms present challenges for data digitalization.
Manual data entry is time-consuming and prone to errors.
Standardized data extraction from non-standard formats is complex.

Purpose of the Study:

To develop a robotic process automation (RPA) based software robot for medical form data digitalization.
To associate extracted data with a specific medical form taxonomy.
To enable efficient data extraction from non-standard handwritten medical forms.

Main Methods:

Utilized UiPath Studio to develop the RPA software robot.
Employed Google Cloud Vision Optical Character Recognition (OCR) for Digital Object Model (DOM) file creation.
Integrated UiPath Machine Learning (ML) API for data extraction.
Applied a data extraction template for non-standard form formats.

Main Results:

Successfully digitalized and extracted data from handwritten medical forms.
Associated extracted data with a predefined medical taxonomy.
Demonstrated the capability to handle non-standard medical form formats.

Conclusions:

The developed RPA robot effectively digitalizes and extracts data from handwritten medical forms.
The system provides a robust solution for handling non-standard medical data formats.
Extracted data can be readily saved into databases or spreadsheets for further analysis.