Su, Kaixiang
Gu, Dongxiao
Yang, Shanlin
Zhu, Kaixuan
Sun, Jiayue
Li, Pengyu
Funding for this research was provided by:
National Natural Science Foundation of China (72131006)
National Natural Science Foundation of China (72131006)
National Natural Science Foundation of China (72131006)
National Natural Science Foundation of China (72131006)
National Natural Science Foundation of China (72131006)
Natural Science Foundation of Anhui Province (2408085J041)
Natural Science Foundation of Anhui Province (2408085J041)
Natural Science Foundation of Anhui Province (2408085J041)
Natural Science Foundation of Anhui Province (2408085J041)
Natural Science Foundation of Anhui Province (2408085J041)
project of the Ministry of Education Philosophy and Social Sciences Laboratory for Data Science and Smart Society Governance of Hefei University of Technology (DSSSG2024P15)
project of the Ministry of Education Philosophy and Social Sciences Laboratory for Data Science and Smart Society Governance of Hefei University of Technology (DSSSG2024P15)
project of the Ministry of Education Philosophy and Social Sciences Laboratory for Data Science and Smart Society Governance of Hefei University of Technology (DSSSG2024P15)
project of the Ministry of Education Philosophy and Social Sciences Laboratory for Data Science and Smart Society Governance of Hefei University of Technology (DSSSG2024P15)
project of the Ministry of Education Philosophy and Social Sciences Laboratory for Data Science and Smart Society Governance of Hefei University of Technology (DSSSG2024P15)
Article History
Received: 10 June 2025
Accepted: 27 April 2026
First Online: 8 May 2026
Competing interests
: The authors declare no competing interests.
: The data used in this study were obtained from Haodf.com, a publicly accessible online health consultation platform in China. All physician-patient interactions analyzed were naturally occurring exchanges that took place independently of this research. Therefore, the dataset falls within the public domain and consists of non-identifiable textual records. Data collection was conducted in compliance with applicable legal and ethical standards. The web crawler adhered strictly to the platform’s Robots Exclusion Protocol and imposed rate limits on requests to avoid server overload. Only publicly available consultation texts and physician profile information were collected; no private or personally identifiable information beyond what was voluntarily disclosed in public consultations was accessed or stored. All data were anonymized prior to analysis by removing any direct identifiers (e.g., names, phone numbers, specific addresses). This study qualifies for exemption from full ethics review in accordance with the Personal Information Protection Law of the People’s Republic of China (PIPL). Specifically, Article 27 of the PIPL stipulates that personal information that has been voluntarily or lawfully disclosed may be processed within a reasonable scope without the need for separate consent. Our research involves the analysis of publicly available, anonymized textual records from online medical consultations, which constitutes processing of already lawfully disclosed information. We have implemented strict measures to ensure that our processing is conducted within a reasonable scope and does not have a significant impact on individuals’ rights and interests, which aligns with the regulatory intent of Article 27 of the PIPL. Nevertheless, a formal application was submitted to the Institutional Review Board (IRB) of the School of Management, Hefei University of Technology. The IRB granted an exemption on December 10, 2025 (Protocol No. HFUT20251210001H). The research procedures thus align with recognized principles of research ethics, including respect for privacy, data security, and the public interest in advancing scientific understanding of online healthcare communication.
: This article does not contain any studies with human participants performed by any of the authors.