Digitization of Medical Data: LLM-Based Data Entry, Cleansing, and Visualization Solutions

Healthcare data management faces several challenges, including errors from manual data entry, variations in data quality, and an increasing need for advanced tools to efficiently handle large datasets. This paper introduces Data-Care, an innovative application designed to automate medical record digitization by leveraging advanced technologies such as Large Language Models (LLMs), Optical Character Recognition (OCR), and NoSQL databases. Together, these technologies significantly reduce the need for manual input, minimizing errors and enhancing data accuracy. A key feature of Data-Care is its data cleansing module, which resolves issues like data inconsistencies and missing values, ensuring the integrity and accuracy of the dataset. Additionally, the application provides a powerful offline visualization tool, enabling healthcare professionals to generate comprehensive reports that support informed decision-making and streamline workflows across various healthcare processes. Extensive performance tests demonstrated that the integration of LLMs and OCR in Data-Care achieved accuracy rates between 76% and 93%, depending on the quality of the input data. These results underscore Data-Care's capacity to improve both the speed and accuracy of data entry, offering a robust solution for the digitization and management of healthcare data.