Objective:In recent years,the economic operating risks of public hospitals are gradually increased.Conducting a scientific assessment of hospital debt risk is crucial to preventing hospitals from falling into financial distress.It aims to evaluate the debt risk of public hospitals in Shenzhen,with a particular focus on the impact of special bonds on debt risk.Methods:Based on the 2022-2023 financial reports and data on special bondsof public hospitals in Shenzhen,factor analysis was employed to assess the debt risk of public hospitals with and without special bonds.Results:Tertiary hospitals,municipal hospitals,specialized hospitals,and general hospitals generally exhibited lower debt risk.Factors related to debt risk primarily included cash flow variables such as medical service income,net assets,and debt repayment capacity indicators,including current ratio and cash ratio.When considering the impact of special bonds,the weight of cash flow indicators and long-term debt repayment capacity indicators increased.Debt risk for municipal hospitals shifted towards higher-risk levels,while district-level hospitals saw a shift towards lower-risk levels,particularly for district-level traditional Chinese medicine hospitals.Conclusion:The debt risk of public hospitals in Shenzhen is significantly influenced by special bonds.It is recommended to strengthen the management of special bond funds,optimize hospital cash flow,and improve debt repayment capacity to reduce debt risk.

Objective:In recent years,the economic operating risks of public hospitals are gradually increased.Conducting a scientific assessment of hospital debt risk is crucial to preventing hospitals from falling into financial distress.It aims to evaluate the debt risk of public hospitals in Shenzhen,with a particular focus on the impact of special bonds on debt risk.Methods:Based on the 2022-2023 financial reports and data on special bondsof public hospitals in Shenzhen,factor analysis was employed to assess the debt risk of public hospitals with and without special bonds.Results:Tertiary hospitals,municipal hospitals,specialized hospitals,and general hospitals generally exhibited lower debt risk.Factors related to debt risk primarily included cash flow variables such as medical service income,net assets,and debt repayment capacity indicators,including current ratio and cash ratio.When considering the impact of special bonds,the weight of cash flow indicators and long-term debt repayment capacity indicators increased.Debt risk for municipal hospitals shifted towards higher-risk levels,while district-level hospitals saw a shift towards lower-risk levels,particularly for district-level traditional Chinese medicine hospitals.Conclusion:The debt risk of public hospitals in Shenzhen is significantly influenced by special bonds.It is recommended to strengthen the management of special bond funds,optimize hospital cash flow,and improve debt repayment capacity to reduce debt risk.

While generative artificial intelligence(AI), exemplified by ChatGPT, demonstrated impressive capabilities in understanding the semantics and context of natural language, and generating coherent and meaningful responses, its performance in the medical field, which required high-level expertise and complex reasoning, remained uncertain. This article aimed to explore the potential applications and challenges of generative AI technology, with ChatGPT as a representative example, in enhancing the capabilities of primary healthcare services. Generative AI, represented by ChatGPT, had potential applications in enhancing primary healthcare services, including clinical assistance in diagnosis, electronic medical record documentation, remote management of chronic patients, and patient education. However, limitations such as the inability to guarantee accuracy, lack of doctor-patient interaction, language barriers, and concerns related to data security, patient privacy, and ethical considerations constrained its practical implementation. Therefore, the application of ChatGPT in improving the capabilities of primary healthcare services required extensive discussion and analysis throughout society. A comprehensive evaluation of potential risks and the establishment of corresponding policies and regulations were necessary to ensure the prudent and responsible introduction and application of ChatGPT, ultimately achieving the goal of empowering primary healthcare services.