Designing an over-the-counter medication electronic health record (OTC-EHR) may help improve OTC usage. An online survey for the conceptual OTC-EHR design examined participant characteristics, attitudes towards obtaining user-shared OTC medication information, health-related application usage, and the inclination to share anonymized health information. Descriptive statistics, tests to assess statistical significance, and text mining were used to analyze the results. The findings revealed that Japanese consumers, particularly those with high eHealth literacy and women, possess relatively positive attitudes towards obtaining user-shared OTC medication information than those with low eHealth literacy (t (280.71) = −4.11, p < 0.001) and men (t (262.26) = −2.78, p = 0.006), respectively. Most consumers own smartphones but do not use health-related applications. A minority held positive attitudes about sharing anonymized health information. The perceived helpfulness of OTC-EHR was positively associated with the usage of health-related applications (χ² (4) = 18.35, p = 0.001) and attitude towards sharing anonymized health information (χ² (3) = 19.78, p < 0.001). The study findings contribute towards OTC-EHR’s design to enhance consumers’ self-medication and reduce risks, while the psychological barriers to sharing anonymized health information should be improved by increasing the OTC-EHR’s penetration rate and engaging in appropriate information design.

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.

As artificial intelligence (AI) becomes increasingly embedded in self-medication, decision-making has evolved into a collaborative process involving users, physicians, and AI systems. This study explores how Generation Z users dynamically allocate decision authority among these actors across six stages of self-medication. Using a mixed-methods approach, we analyzed survey data from 87 participants and conducted in-depth interviews with six users. Results revealed significant stage-based differences (p < 0.001): physician intervention dominated medication-related stages (p < 0.001), while AI support and self-decision alternated across information-seeking and reflective stages (p < 0.001). Three user types, cautious, experience-driven, and dependence-oriented, exhibited distinct collaboration patterns (p < 0.001). These findings demonstrate that Generation Z users do not follow a single decision strategy but dynamically negotiate human–AI–physician collaboration. We argue that self-medication interaction systems should adopt stage- and user-adaptive collaboration strategies to support precision intervention and improve user experience.

Conventional evaluations of indoor environmental quality (IEQ) in office spaces are typically disproportionately influenced by expert experience, often overlooking the cognitive gap between decision makers (experts) and users (employees). To quantify and explain this discrepancy, this study develops a comprehensive evaluation framework including 20 IEQ indicators, grounded in Maslow’s hierarchy of needs. Using the Shenzhen Science Park as a case study, evaluation data were collected from 13 experts and 432 employees. The Analytic Hierarchy Process (AHP) and the Kano model were applied to calculate expert weights and employees’ nonlinear sensitivities, respectively, followed by the construction of an optimization matrix via Importance–Performance Analysis (IPA). The results reveal a notable cognitive gap: experts prioritize foundational physical elements regarding spatial technology, whereas employees place greater emphasis on factors such as privacy protection and flexible layouts. Both groups concur that “noise interference” and “lack of privacy” are the primary shortcomings of open-plan offices. Prospective assessments indicate that embodied AI-enabled robots currently remain in a “early adoption phase,” with employees showing no functional dependency on them. This study confirms that merely improving building physical performance does not proportionally translate to increased employee satisfaction. Spatial optimization should adopt a human-centric approach, emphasizing acoustic control and the reconfiguration of privacy boundaries to enhance the scientific allocation of resources.