Preemptive governance of online public opinion risks in public hospitals
Year: 2026
Type: editorial
Abstract: The rapid proliferation of mobile internet, short-video platforms, and generative artificial intelligence (AI) has made information dissemination increasingly immediate, fragmented, emotionally charged, and community-segmented.By December 2025, China had 1.125 billion internet users (internet penetration rate hit 80.1%), with shortvideo and generative AI users reaching 1.074 billion and 602 million, respectively (1).Public hospitals face unique vulnerabilities in this new media environment.They are characterized by high technical barriers, asymmetric physician-patient information, and intense public visibility and scrutiny (2).These features render even minor oversights prone to escalation.A small incident can rapidly evolve into a major online public opinion through online contagion, ultimately undermining hospital operations and eroding physician-patient trust.Recent years have seen frequent incidents involving service conflicts, privacy breaches, medical disputes, and medical anti-corruption (3,4).The conventional "blocking, deleting, and suppressing" or post-hoc firefighting approach no longer suits the current communication landscape.The 2026 National Health Work Conference mandated "improving mechanisms for preventing and managing online public opinion" ( 5), yet a gap persists between policy formulation and institutional implementation.Preemptive governance-shifting public opinion management upstream for source-based risk prevention-has become a research priority in hospital administration.This paper systematically reviews the domestic literature on this topic, evaluating theoretical applications, practical challenges, and governance pathways, while identifying research gaps and proposing future directions.Theoretically, Heath's 4R model-reduction, readiness, response, and recovery-is the most widely applied framework of public opinion research in healthcare, covering the crisis lifecycle (6).Domestic studies have introduced it into public hospital settings through tertiary hospital case studies, providing a foundational analytical framework (2,3,7,8).However, its direct application to online public opinion in the medical field reveals three fundamental misfits.First, the model was developed in the traditional media era and inadequately addresses emerging online communication scenarios.Second, risk identification in the reduction phase focuses on organizational and
Authors: Yuhong Chen, Ting Yu
Language: English
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7213899298