Improving Healthcare Management Tools in Uzbekistan With The Help of Automatic Speech Recognition: Time, Quality, Load, and Data Optimization in Healthcare Settings

Authors

  • Teshaev Rajabbek Kaxramonovich PhD Researcher, Westminster International University in Tashkent
  • Bakhtiyor Islamov Anvarovich Professor, Doctor of Economic Sciences, Tashkent branch of Plekhanov Russian Economic University, Republic of Uzbekistan

DOI:

https://doi.org/10.31150/ajebm.v8i5.3571

Keywords:

automatic speech recognition, voice-to-text enabled typing, medical data management, electronic health records, digitalization, healthcare management

Abstract

Efficient documentation in healthcare is vital for service quality, yet electronic health records (EHRs) often impose time, cognitive, and usability burdens on physicians. In Uzbekistan, challenges in manual EHR entry, typographical errors, and copy-pasting hinder clinical productivity and data integrity. While global studies highlight these limitations, there is limited empirical research in post-Soviet healthcare settings on the role of automatic speech recognition (ASR) in overcoming them. This study evaluates the effectiveness of ASR tools in improving documentation time, accuracy, and medical data richness within Uzbek clinical environments. Using a mixed-methods quasi-experimental design across two medical institutions, ASR reduced documentation time by 41%, decreased typographical errors by 17.6%, and increased the volume of recorded medical data by 28%. Physicians also reported a 7.5% decline in copy-paste behavior and noted improved satisfaction and workflow efficiency. This research provides the first quantified national estimate of ASR’s potential in saving over 108 million hours annually, translating into approximately $292 million in cost reductions for Uzbekistan’s healthcare system. ASR integration not only boosts operational efficiency but also enhances patient safety and clinical decision-making through richer, error-reduced documentation. The study supports piloting ASR in diverse medical settings and embedding digital literacy in healthcare education to ensure adoption across age groups, contributing to broader digital transformation in emerging healthcare systems.

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Published

2025-05-09

How to Cite

Rajabbek Kaxramonovich, T. ., & Islamov Anvarovich, B. . (2025). Improving Healthcare Management Tools in Uzbekistan With The Help of Automatic Speech Recognition: Time, Quality, Load, and Data Optimization in Healthcare Settings. American Journal of Economics and Business Management, 8(5), 2171–2180. https://doi.org/10.31150/ajebm.v8i5.3571

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