eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions

Over-the-counter medication electronic health records (OTC-EHRs) play a significant role in users’ self-medication practices. In this study, we consider the potential advantages of wearable smart devices in health management, along with the information processing capabilities of artificial intelligence (AI), and we propose a conceptual design for an AI-assisted wearable OTC-EHR system. Our objective was to systematically explore the relationship between eHealth literacy, users’ attitudes, and willingness to use the proposed system, as well as to discuss AI interventions. Internet users from China participated in an online survey examining eHealth literacy, subjective attitudes, and motivation to use this conceptual design. Descriptive statistical, correlation, difference, and regression analyses were conducted on 372 valid responses to test the research hypotheses. The results showed that the wearable-device-based OTC-EHR system with AI assistance was accepted by most responders and positively associated with eHealth literacy, which was, in turn, associated with decision-making preferences. This study suggests that AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance.

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Journal
Systems
Published
2025-11-27
DOI
https://doi.org/10.3390/systems13121070
Primary Topic
Electronic Health Records Systems
Type
article
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article

eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions

Guyue Tang, Shinichi Koyama, Zhidiankui Xu
Systems
Electronic Health Records Systems
article

eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions

Guyue Tang, Shinichi Koyama, Zhidiankui Xu
article en

Abstract

Over-the-counter medication electronic health records (OTC-EHRs) play a significant role in users’ self-medication practices. In this study, we consider the potential advantages of wearable smart devices in health management, along with the information processing capabilities of artificial intelligence (AI), and we propose a conceptual design for an AI-assisted wearable OTC-EHR system. Our objective was to systematically explore the relationship between eHealth literacy, users’ attitudes, and willingness to use the proposed system, as well as to discuss AI interventions. Internet users from China participated in an online survey examining eHealth literacy, subjective attitudes, and motivation to use this conceptual design. Descriptive statistical, correlation, difference, and regression analyses were conducted on 372 valid responses to test the research hypotheses. The results showed that the wearable-device-based OTC-EHR system with AI assistance was accepted by most responders and positively associated with eHealth literacy, which was, in turn, associated with decision-making preferences. This study suggests that AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance.

SystemsVol. 13(12)
Jiangnan University (CN), University of Tsukuba (JP), Shenzhen University (CN), Institute of Applied Biochemistry (JP)
Openalex Percentile: Top 57%
Electronic Health Records Systems
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eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions — Guyue Tang, Shinichi Koyama, et al. · Systems (2025) | TGRS Research Map | TGRS