Enhancing Healthcare Data Entry Efficiency and Accuracy through Voice Assistant Systems: A Case Study of Obijackson Hospital Okija

Okeke Ogochukwu C. and Ezenwegbu Nnamdi Chimaobi
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Okeke Ogochukwu C.: Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli AN, NG
Ezenwegbu Nnamdi Chimaobi: Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli AN, NG

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 10, 277-286

Abstract: Traditional data entry methods are often time-consuming and prone to errors, negatively impact patient care and administrative workflows. To address these challenges, this research proposed a voice-assisted data entry system that leverages advanced speech recognition and natural language processing technologies. Object Oriented Analysis and Design Methods were used to implement a system that allows healthcare providers to input patient data and medical information through voice commands, streamlining the data entry process and reducing the cognitive load on healthcare professionals. The system’s design incorporates user-centred principles, ensuring it is intuitive and seamlessly integrated into existing healthcare IT infrastructures. Key features include real-time speech-to-text conversion, contextual understanding of medical terminology, and robust data security measures to protect patient information. The implementation phase involved rigorous testing in a simulated healthcare environment, assessing the system’s accuracy, speed, and user satisfaction. Results indicated significant improvements in data entry efficiency and reduced error rates compared to manual entry methods. Feedback from healthcare professionals highlighted the system’s potential to enhance productivity and patient care quality. This dissertation contributes to the field of healthcare informatics by providing a practical solution to a critical problem, demonstrating the feasibility and benefits of voice-assisted technologies in medical data management. Future work will explore further refinements and the potential for broader adoption across diverse healthcare settings.

Date: 2024
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