Virtual healthcare bot (VHC-Bot): a Person-centered AI chatbot for transforming patient care and healthcare workforce dynamics

Abstract This study addresses the growing role of Virtual Healthcare (VHC) in mitigating the global shortage of skilled healthcare workers and explores how Artificial Intelligence (AI) can empower clinicians by providing rapid, reliable information at the point of care. However, the proliferation of AI in healthcare poses risks of potential deskilling of clinicians’ judgment and the inability of some non-AI platforms to deliver Person-Centered (PC) care. These shortcomings may lead to unsafe self-diagnosis practices. This paper introduces VHC-Bot, an AI-driven PC VHC platform designed to strike a balance between patient autonomy, healthcare worker expertise, and AI support to deliver accurate, efficient, and personalized care. It emphasizes collaborative decision-making, effective communication, and knowledge-sharing to enhance clinical skills. The study leverages advanced AI models to design the VHC-Bot platform and integrates PC care principles. Key components include natural language processing for effective communication, diagnostic algorithms for precise symptom evaluation, and machine learning models to adapt to individual patient needs. Performance evaluation methods include clinical simulation testing, patient satisfaction surveys, and workflow efficiency analysis. Results indicate significant improvements in diagnostic accuracy, consultation times, and clinician-patient communication using the platform, which fosters collaboration among healthcare professionals, enhancing their clinical judgment and maintaining decision-making authority. Furthermore, patient satisfaction scores demonstrated marked improvement due to the personalized and accessible care provided by VHC-Bot. VHC-Bot delivers high-quality, efficient care while safeguarding human expertise in clinical judgment. This approach ensures accessible healthcare, efficient, and human-centred, setting a benchmark for future AI-integrated VHC systems.

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Publication Details

Journal
Network Modeling Analysis in Health Informatics and Bioinformatics
Published
2025-06-17
DOI
https://doi.org/10.1007/s13721-025-00537-x
Citations
3
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.96
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article

Virtual healthcare bot (VHC-Bot): a Person-centered AI chatbot for transforming patient care and healthcare workforce dynamics

Hessah A. Alsalamah, Chang‐Tien Lu, Sara A. Alsalamah, Haytham A. Sheerah et al.
3 citations
Network Modeling Analysis in Health Informatics and Bioinformatics
Artificial Intelligence in Healthcare and Education
1.96
article

Virtual healthcare bot (VHC-Bot): a Person-centered AI chatbot for transforming patient care and healthcare workforce dynamics

Hessah A. Alsalamah, Chang‐Tien Lu, Sara A. Alsalamah, Haytham A. Sheerah, Kurt Luther, Shada Alsalamah
article en
3 citations

Abstract

Abstract This study addresses the growing role of Virtual Healthcare (VHC) in mitigating the global shortage of skilled healthcare workers and explores how Artificial Intelligence (AI) can empower clinicians by providing rapid, reliable information at the point of care. However, the proliferation of AI in healthcare poses risks of potential deskilling of clinicians’ judgment and the inability of some non-AI platforms to deliver Person-Centered (PC) care. These shortcomings may lead to unsafe self-diagnosis practices. This paper introduces VHC-Bot, an AI-driven PC VHC platform designed to strike a balance between patient autonomy, healthcare worker expertise, and AI support to deliver accurate, efficient, and personalized care. It emphasizes collaborative decision-making, effective communication, and knowledge-sharing to enhance clinical skills. The study leverages advanced AI models to design the VHC-Bot platform and integrates PC care principles. Key components include natural language processing for effective communication, diagnostic algorithms for precise symptom evaluation, and machine learning models to adapt to individual patient needs. Performance evaluation methods include clinical simulation testing, patient satisfaction surveys, and workflow efficiency analysis. Results indicate significant improvements in diagnostic accuracy, consultation times, and clinician-patient communication using the platform, which fosters collaboration among healthcare professionals, enhancing their clinical judgment and maintaining decision-making authority. Furthermore, patient satisfaction scores demonstrated marked improvement due to the personalized and accessible care provided by VHC-Bot. VHC-Bot delivers high-quality, efficient care while safeguarding human expertise in clinical judgment. This approach ensures accessible healthcare, efficient, and human-centred, setting a benchmark for future AI-integrated VHC systems.

Network Modeling Analysis in Health Informatics and BioinformaticsVol. 14(1)
Imam Mohammad ibn Saud Islamic University (SA), King Saud University (SA), Ministry of Health (SA), Al Yamamah University (SA), Virginia Tech (US)
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
1.96
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