Universal health coverage (UHC) is driving the global health agenda. Many countries have embarked on national policy reforms towards this goal, including China. In 2009, the Chinese government launched a new round of healthcare reform towards UHC, aiming to provide universal coverage of basic healthcare by the end of 2020. The year of 2019 marks the 10th anniversary of China’s most recent healthcare reform. Sharing China’s experience is especially timely for other countries pursuing reforms to achieve UHC. This study describes the social, economic and health context in China, and then reviews the overall progress of healthcare reform (1949 to present), with a focus on the most recent (2009) round of healthcare reform. The study comprehensively analyses key reform initiatives and major achievements according to four aspects: health insurance system, drug supply and security system, medical service system and public health service system. Lessons learnt from China may have important implications for other nations, including continued political support, increased health financing and a strong primary healthcare system as basis.

Universal health coverage (UHC) has been identified as a priority for the global health agenda. In 2009, the Chinese government launched a new round of healthcare reform towards UHC, aiming to provide universal coverage of basic healthcare by the end of 2020. We conducted a secondary data analysis and combined it with a literature review, analysing the overview of UHC in China with regard to financial protection, coverage of health services and the reported coverage of the WHO and the World Bank UHC indicators. The results include the following: out-of-pocket expenditures as a percentage of current health expenditures in China have dropped dramatically from 60.13% in 2000 to 35.91% in 2016; the health insurance coverage of the total population jumped from 22.1% in 2003 to 95.1% in 2013; the average life expectancy increased from 72.0 to 76.4, maternal mortality dropped from 59 to 29 per 100 000 live births, the under-5 mortality rate dropped from 36.8 to 9.3 per 1000 live births, and neonatal mortality dropped from 21.4 to 4.7 per 1000 live births between 2000 and 2017; and so on. Our findings show that while China appears to be well on the path to UHC, there are identifiable gaps in service quality and a requirement for ongoing strengthening of financial protections. Some of the key challenges remain to be faced, such as the fragmented and inequitable health delivery system, and the increasing demand for high-quality and value-based service delivery. Given that China has committed to achieving UHC and ‘Healthy China 2030’, the evidence from this study can be suggestive of furthering on in the UHC journey and taking the policy steps necessary to secure change.

Background: Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China. Objective: This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students’ acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals. Methods: A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression. Results: A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693 , 91.05%) and increasing learning efficiency (594/693, 85.71%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05). Conclusions: Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology.

Background: As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics. Objective: This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission. Methods: An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens’ knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification. Results: Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ≥50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non–health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities’ social media accounts with a trust rating score greater than 1 (P≤.04) were associated with a lack of cross-verification of information. Conclusions: The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities’ star power, and increase Chinese netizens’ awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information.

Background: With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image‐assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application.Methods: Our study encompassed a cohort of 23 336 patients who underwent chest low‐dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI‐assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules.Results: The AI‐assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001).Conclusions: Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI‐assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

Aging population and other factors have led to a rapid rise in cancer incidence in China. However, under the influence of traditional perception of diseases, deaths and economic factors, many patients who are unresponsive to radical treatment are still adherent to excessive and unnecessary treatment, which may lead to poor quality of life (QoL) and increase unnecessary medical burden. Compare the difference of the quality of life and cost-utility value between patients who received palliative care (PC) and patients who were adherent to conventional anticancer treatment (CAT) and provides empirical evidence of clinical and economic value for hospital-based PC. Chinese Quality of Life Questionnaire (CQLQ) Scale was used to collect advanced cancer patients’ QoL on admission and discharge days. Paired and independent samples’ statistical analysis were used to compare inter- and intra- QoL between PC and CAT group. Delphi and Analytic Hierarchy Process were used to weight QoL scores and converted the QoL to quality-adjusted life years (QALYs). Propensity Score Matching (PSM) for 1:1 was used to compare average hospitalization expenses between two groups. The expense per QALYs was used for Cost-Utility analysis between the two treatments. A total of 248 hospitalized patients diagnosed with metastatic disease at stage IV were recruited from West China Fourth Hospital between January 2018 and August 2018, including 128 patients receiving PC and 120 patients receiving CAT. Although both treatments had positive effects on improving QoL for patients, the QoL in the PC group were significantly higher than that in the CAT group (55.90 ± 18.80 vs 24.00 ± 8.60, t = 7.51, p < 0.05). The QALY (days) of pre- and post- treatment increased by 55.9 and 24.0 days in PC and CAT group respectively. Compared average hospitalization expense in 613 pairs of advanced cancer inpatients after PSM 1:1, the per capita expense of PC group was higher (13,743.5 ± 11,574.1 vs 11,689.0 ± 8876.8, t = 3.44, p < 0.05), while each unit of QALYs paid by PC group was only 50% of that paid by those receiving CAT. PC played a positive role in improving the QoL for patients diagnosed with advanced cancer and alleviating economic burdens of both patient families and the society from the viewpoint of cost-utility. Our findings imply that PC should be recognized as a proactive care model in China that helps patients with some terminal diseases.