Bridging Institutional Maturity and Public Attention: A Mixed-Methods Study of Telemedicine Institutionalization in Japan

Open Access
Article
Conference Proceedings
Authors: Hanshu WangShizu GotohXiuzhu Gu
Abstract

Telemedicine in Japan is undergoing a critical transition from administrative guidance to a formal legal system, yet alignment between institutional maturity (supply side) and public attention (demand side) remains unclear. This study adopted a mixed-methods approach, combining the “Regulatory Framework for Telemedicine” (7 categories) with large language model (LLM)-enhanced topic modeling, to analyze policy documents and 68,481 X (formerly Twitter) posts related to telemedicine from 2019 to 2025. Then, the institutional maturity and public attention across categories were compared. Results revealed distinct interaction patterns between institutional maturity and public attention. First, a stabilization pattern was observed in the Governance and Actions of health institutions and teams categories: improvements in institutional maturity mitigated initial public attention, leading to a transient rise in attention that subsequently declined as practices became normalized. Conversely, a tension pattern persisted in Regulatory aspects, where limited growth institutional maturity failed to address sustained high public attention. Furthermore, a significant misalignment pattern was identified in Cross-cutting principles and human rights, where institutional maturity lagged rapidly rising public attention. We recommend that policymakers prioritize strengthening legal support for vulnerable groups and scenarios attracting high public attention, and transform temporary administrative guidance into formal legislation to bridge gaps between institutional design and public expectations. The developed methods provide a transferable approach for telemedicine institutionalization research in other regions.

Keywords: Telemedicine, Institutional Maturity, Public Attention, Mixed Methods, Social Media Topic Modeling, Large Language Model (LLM)

DOI: 10.54941/ahfe1007739

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