Next-Gen Healthcare: Virtual Health Assistants and Handwritten Prescription Analysis

Chronic conditions such as diabetes, hypertension, and cardiovascular diseases are major contributors to morbidity and mortality around the world. Successfully managing these health issues requires continuous monitoring, adherence to medication regimens, lifestyle changes, and thorough patient education. Nonetheless, healthcare systems often struggle to provide consistent and personalized care, especially for those with long-term health conditions. In this context, the integration of Artificial Intelligence-driven Virtual Health Assistants (VHAs) presents a revolutionary method for enhancing the management of chronic diseases. These VHAs promote real-time engagement and support for patients, assisting them in effectively navigating their health challenges.Furthermore, the implementation of a system for analyzing handwritten prescriptions boosts healthcare efficiency by employing AI-based Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques. This advanced system proficiently extracts and interprets handwritten prescriptions, significantly reducing the likelihood of medication errors and enhancing the accuracy of prescriptions. By digitizing handwritten text, it optimizes workflows and improves the overall standard of care.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-24
DOI
https://doi.org/10.5281/zenodo.16679647
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Next-Gen Healthcare: Virtual Health Assistants and Handwritten Prescription Analysis

Zenodo (CERN European Organization for Nuclear Research)
Handwritten Text Recognition Techniques
article

Next-Gen Healthcare: Virtual Health Assistants and Handwritten Prescription Analysis

article en

Abstract

Chronic conditions such as diabetes, hypertension, and cardiovascular diseases are major contributors to morbidity and mortality around the world. Successfully managing these health issues requires continuous monitoring, adherence to medication regimens, lifestyle changes, and thorough patient education. Nonetheless, healthcare systems often struggle to provide consistent and personalized care, especially for those with long-term health conditions. In this context, the integration of Artificial Intelligence-driven Virtual Health Assistants (VHAs) presents a revolutionary method for enhancing the management of chronic diseases. These VHAs promote real-time engagement and support for patients, assisting them in effectively navigating their health challenges.Furthermore, the implementation of a system for analyzing handwritten prescriptions boosts healthcare efficiency by employing AI-based Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques. This advanced system proficiently extracts and interprets handwritten prescriptions, significantly reducing the likelihood of medication errors and enhancing the accuracy of prescriptions. By digitizing handwritten text, it optimizes workflows and improves the overall standard of care.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 54%
Handwritten Text Recognition Techniques
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