Implementation evaluation demonstrates reduced manual data entry time in healthcare institutions, highlighting the utility of AI-driven clinical workflow automation.
This thesis investigates the implementation of artificial intelligence (AI)-based optical character recognition (OCR) and automated data visualization technologies to optimize manual data entry processes in healthcare institutions. The study aims to develop models that save medical staff time, minimize mechanical errors caused by human factors, and accelerate the analysis of medical histories. Within the methodology, the operational principles of OCR systems based on Deep Learning and Natural Language Processing (NLP) algorithms were analyzed. The results indicate that integrating these technologies reduces manual data entry time by 70-80% and enables rapid monitoring of disease dynamics using automated dashboards. In conclusion, it is substantiated that training future medical professionals in digital technologies and AI tools is a crucial foundation for improving the quality of healthcare services.
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Turdimuratov et al. (2026) studied this question.
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