In response to the challenges posed by the profound integration of generative artificial intelli-gence(AI)into medical education,this consensus proposes a logically coherent,medically distinctive,for-ward-looking,and operable AI proficiency framework for medical educators(competency items of medical edu-cators'AI proficiency,CAIP-ME).Through systematic literature review,preliminary framework construction,multiple rounds of expert pre-study,and a structured Delphi method involving extensive consultations with 60 in-terdisciplinary experts,the core competency items and assessment standards for AI proficiency among medical ed-ucators were demonstrated and calibrated.The framework encompasses five core dimensions and 25 specific com-petency items.The five dimensions are value recognition and ethical foundation,technical understanding and tool application,teaching integration and innovative practice,learning assessment and precise empowerment,and professional development and ecosystem co-construction.Competency items are categorized into 11 foundational competency items essential for all educators and 14 developmental competency items for those pursuing excel-lence.Each competency item is described in terms of its conceptual definition and key behavioral manifestations,accompanied by observable assessment indicators.This consensus aims to provide a scientific basis for the profes-sional development of medical educators and the faculty building in medical institutions,while establishing a key reference standard for educator competency development in the context of digital transformation in medical education.

Systematized nomenclature of medicine-clinical terms (SNOMED CT), one of the most comprehensive clinical terminology systems, is pivotal in enhancing healthcare interoperability, clinical data governance, and medical artificial intelligence (AI) development globally. In China, with the rapid growth of large-scale models and an increasing emphasis on transforming the intrinsic value of healthcare data, the absence of a nationally unified clinical terminology standard poses significant challenges. This commentary provides an in-depth analysis of the benefits of SNOMED CT for global healthcare, examines the critical deficiencies in Chinese healthcare big data and AI development due to the lack of standardized terminology, and outlines the technical, administrative, and educational challenges encountered in deploying SNOMED CT within Chinese environments. Special emphasis is laid on the potential of advanced large language models in facilitating the mapping of Chinese clinical data to SNOMED CT. We further discuss the necessity of high-quality data standardization in advancing medical AI in China. Finally, key conclusions and a roadmap for overcoming these challenges are proposed.

BACKGROUND:Chronic kidney disease (CKD) is a global health challenge. Body mass index (BMI) fails to capture the heterogeneity of fat distribution and metabolic status in obesity. We aimed to investigate whether integrated obese-metabolic-anthropometric phenotypes, which simultaneously consider adiposity, metabolic health, and body shape, provide a superior framework for identifying individuals at high risk of CKD. METHODS:This prospective cohort study included 343,993 participants from the UK Biobank without pre-existing CKD. Obese-metabolic-anthropometric phenotypes were defined by integrating BMI, a metabolic health score, and body shape (based on A Body Shape Index and Hip Index). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CKD. Population attributable risk (PAR) was calculated to quantify the CKD burden attributable to different phenotypes. K-modes cluster analysis classified individuals into four distinct subtypes. RESULTS:Over a mean follow-up of 13.6 years, 16,037 incident CKD cases were recorded. Compared to the reference group (metabolically healthy non-obese with slim shape), both metabolically unhealthy obese wide-shaped (MUOW) and apple-shaped (MUOA) phenotypes demonstrated substantially elevated CKD risk, with fully adjusted HRs of 2.26 (95% CI: 2.10-2.43) and 2.68 (95% CI: 2.48-2.89), respectively. PAR analysis revealed that the integrated phenotype contributed most to the population-level CKD burden (PAR: 29.3%, 95% CI: 20.8-38.3%), far exceeding the contribution of any single component. Cluster analysis further delineated a high-risk cluster characterized by co-existing obesity and metabolic dysfunction, which exhibited an 89% increased risk of CKD (HR: 1.89, 95% CI: 1.80-1.97). CONCLUSION:The confluence of obesity, metabolic dysfunction, and an adverse body shape synergistically substantially elevates CKD risk. Moving beyond BMI to multidimensional phenotyping enables precision identification of high-risk individuals for targeted preventive strategies.

Background:Artificial intelligence (AI) is transforming healthcare, demanding reevaluation of medical education. China's "New Medical Education" initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards, rapid technological evolution, and insufficient localized ethical norms. Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students. Methods:A multidisciplinary (including medical education, clinical medicine, medical AI, public health, and medical ethics) expert group (n = 32) developed an initial competency list based on the "Knowledge-Skills-Attitude" Medical Competency Model. Two Delphi rounds (100% response rate; consensus threshold: mean ≥ 4.0, CV ≤ 0.25) refined the framework. Core competencies were prioritized via Analytic Hierarchy Process (AHP). The final consensus document was established after multiple expert group meetings. Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into professional knowledge, clinical practice, research, and health management. It comprises a 21-item Competencies of AI Proficiency (CAIP) list across knowledge (eight indicators), skills (seven indicators), and attitude (six indicators) dimensions. Key competencies prioritized include understanding AI's role in multidisciplinary knowledge integration (CAIP3), identifying AI output biases (CAIP4), understanding health data governance (CAIP2), maintaining physician-led AI-assisted diagnosis (CAIP16), and identifying AI diagnostic biases (CAIP12). A multi-modal assessment framework is recommended, including paper-based/computerized tests for knowledge, situational judgment tests (SJTs) for attitudes, and objective structured clinical examinations (OSCEs) with a specific "AI Clinical Decision Conflict Scoring Scale" for skills. A multi-stage dynamic assessment system ("Pre-enrollment-Pre-clinical-Post-clinical") is proposed for longitudinal tracking. Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years, constructing an integrated curriculum covering fundamental principles, advanced large model applications (e.g., prompt engineering, agent development), and ethical considerations, supported by a "digital twin hospital platform." Conclusion:This consensus provides authoritative, China-specific guidance for defining and assessing medical students' AI literacy, adhering to national policies and regulations. It offers a core action framework for optimizing AI integration into medical education, fostering future healthcare professionals proficient in both AI technology and medical humanism, with a commitment to dynamic updating to adapt to evolving AI advancements.

Background Plant-based diets have been consistently associated with a lower risk of several individual cardiometabolic diseases (CMDs). However, whether such dietary patterns differentially influence the progression from health to first-occurrence cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM), and ultimately mortality remains unclear. Methods The present study analyzed data from 83,610 participants in the UK Biobank cohort who were not diagnosed with diabetes, ischaemic heart disease (IHD), or stroke at baseline. Multi-state models were employed to examine the impact of plant-based diets on trajectories of cardiometabolic multimorbidity. Results During a median follow-up period of 15.61 years, the median age of the participants at baseline was 57 years (IQR: 50 years-62 years), 42.72% were male. 9298 participants developed at least one CMD, 1,045 participants progressed to CMM, and 4169 participants ultimately died. The finding of the multi-state model suggest that, compared with Q1, both the overall plant-based diet index (PDI)[HR (95%CI): 0.88 (0.83, 0.94) for baseline to FCMD, 0.85 (0.83, 0.94) for baseline to CMM] and the healthy plant-based diet index (hPDI) [HR (95%CI): 0.60 (0.41, 0.89) for baseline to FCMD, 0.79 (0.53, 1.17) for baseline to CMM] were negatively associated with the risk of transitioning from health to FCMD and CMM. When grouping FCMD into disease-specific analyses, it was found that the three plant-based indices also exerted differential effects on the transition from health to diabetes. Conclusion In the progression of CMM, high adherence of PDI and hPDI has been demonstrated to reduce the risk of transitioning from CMD-free to FCMD, particularly in diabetes, and lowers the risk of CMM with a much lower incidence risk from CMD-free to CMM compared to CMD-free to FCMD. The present study hypothesizes that both hPDI and unhealthy plant-based index (uPDI) are associated with the risk from baseline to death.

To cultivate composite medical professionals capable of adapting to the development of intel-ligent healthcare,this consensus is grounded in the competency-based medical education,integrating the compe-tency model and Miller's pyramid of clinical competence.A two-round Delphi method involving a multidiscipli-nary expert panel was conducted,combined with a systematic literature review,to develop a 21-indicator artifi-cial intelligence(AI)literacy competency list for medical students across three domains:knowledge(8 indica-tors),skills(8 indicators),and attitudes(5 indicators).Furthermore,the consensus proposes a practical assessment system:standardized testing for the knowledge domain,situational judgment tests for the attitudes domain,and objective structured clinical examinations incorporating AI-related scenarios for the skills domain.In addition,a longitudinal assessment strategy spanning the phases of admission,preclinical training,and clini-cal training is recommended.The competency list and assessment framework established in this consensus demon-strate strong scientific rigor,authority,and practical applicability,and can serve as an important reference for medical schools seeking to advance the deep integration of AI and medical education and to cultivate composite medical talents suited to the era of intelligent healthcare.

Mental health issues affect populations worldwide, with depression, schizophrenia, and dementia being particularly prevalent in China, where the China Mental Health Survey (CMHS) reported a 7.4% lifetime prevalence of mood disorders. Mental disorders have become a leading cause of disability. While the widespread adoption of electronic medical records (EMRs) has significantly improved healthcare efficiency and resource allocation, the sensitivity of medical data poses serious privacy breach risks. Many patients withhold medical information due to data security concerns, increasing the risk of treatment discontinuation. Currently, the lack of unified management policies and technical standards for electronic health records (EHRs) has led to frequent unauthorized access, leaks, and illegal trading of data, exacerbating doctor-patient conflicts and societal stigma against individuals with mental illness. This study developed a novel personally identifiable information (PII) desensitization protocol (EPPDI) to mitigate privacy risks through comprehensive database scanning (as opposed to traditional field-specific desensitization). The protocol incorporates the following technological innovations: (1) Expansion of a lexicon of 20 mental health-related keywords using a Word2Vec vector space model; (2) Application of regular expressions to replace sensitive information and its surrounding 10 characters with asterisks ( * ). Among 1,235,651 patients (8,016,263 records), the EPPDI protocol achieved 97.60% precision and 95.40% recall, with a privacy protection efficacy rate of 97.85%. Diagnosis records (31.84%) and medication data (45.41%) were identified as primary leakage sources. Regional disparities were notable, with Beijing showing a PD identification rate of 21.06%, far exceeding Qinghai’s 1.66%. Regarding data utility preservation, among 2,000 pieces of PDI patient information, 48 (2.4% false positive rate) contained no sensitive content, while 92 (4.6% false negative rate) pieces of non-PDI patient information included sensitive data among 2,000 pieces of non-PDI patient information. The EPPDI protocol addresses challenges such as ambiguity in Chinese terminology and adaptation to unstructured narratives, providing a technical framework for implementing China’s Personal Information Protection Law in mental health. Future efforts should focus on balancing privacy protection with research needs through dynamic, tiered desensitization approaches.

The United Nations Sustainable Development Goal (SDG 3) aims to strengthen healthcare systems, combat chronic and infectious diseases, and improve global health. However, chronic diseases pose significant public health challenges, straining healthcare resources and escalating economic burdens. In China, they affect 180 million people, account for over 90 % of the national disease burden, and are the leading cause of mortality. Community chronic disease management faces challenges such as limited capacity, uneven resource allocation, and weak information systems. Despite policies to improve primary healthcare, outcomes remain modest due to implementation gaps. Addressing these issues requires creating "multidimensional value" through collaboration among doctors, patients, families, and communities. This framework emphasizes functional value (efficiency), social value (community ties), emotional value (well-being), and health value (better outcomes). However, most research narrowly focuses on doctor-patient collaboration, overlooking broader dynamics involving families and community healthcare providers. By explicitly exploring the goals and collaborative roles of doctor-patientfamily value co-creation in community chronic disease management, we aim to develop well informed strategies to enhance interaction and resource integration, offering insights for China and scalable solutions for global health.

Objective:To characterize the sequential patterns and transition timelines of chronic disease comorbidities in population with obesity. Methods:We analyzed population with obese from the English Longitudinal Study of Ageing, including 22,355 independent participants for using association rule mining (ARM) to identify comorbidity patterns and 92,092 person-observations to analyze disease progression pathways and transition probability by multi-stage Markov chain (MMC). Health burden was compared between different onset disease. Results:ARM identified cardiovascular (CVD), metabolic (MTD), and skeletal-muscular disease (SMD) as the most prevalent disease trio. MMC revealed 40% of obese individual will develop a chronic disease within 5 years, and nearly 30% with MTD or CVD will develop to the trio within 10 years. Progression times to the trio differed significantly based on initial disease type (p < 0.003), with MTD onset being the fastest progression (3.89 years). SMD onset was associated with the most adverse health burden profile, including the highest depression rate (6.3%), poorest sleep quality (77.0%), and substantial work limitations (74.0%). Conclusions:These findings establish quantifiable transition probabilities and timelines for chronic disease progression, emphasizing the important role of onset disease and contributing empirical evidence for the sequential nature of multimorbidity development.

Climate change poses a significant threat to global health. It exacerbates existing health challenges and generates new ones. Therefore, innovative solutions to mitigate and adapt to its adverse effects are urgently required. This article explores the potential of digital health technologies to address the challenge posed by climate change-related health issues. It discusses their dual functionality of diminishing the carbon footprint of healthcare services and increasing understanding and governance of climate-sensitive diseases. Notably, with advanced technologies such as Generative medical AI (GMAI) presenting environmental concerns like substantial energy consumption during data processing and the generation of electronic waste, it is essential to underscore the significance of their responsible development and implementation of these technologies. This will ensure that the benefits of digital health technologies can be maximized while minimizing their ecological drawbacks. This study, therefore propose, a framework for leveraging digital health technologies to support climate change adaptation, including disease surveillance, telemedicine, patient support systems, and public awareness campaigns.

Background Epidemiological studies have shown that social isolation, which is prevalent in older adults, is associated with a range of adverse health outcomes, but the prevalence of and trends in regard to social isolation remain ambiguous in China. The aim of this study was to elucidate the trends regarding the prevalence of social isolation among middle-aged and older adults in China from 2011 to 2018 and to further identify associated risk factors. Methods A repeated cross-sectional study, The data were derived from panel sample data of four waves conducted from May 2011 to August 2018 in the nationally representative China Health and Retirement Longitudinal Study (CHARLS) using multistage probability sampling. Social isolation was ascertained by the five item Steptoe Social Isolation Index. The potential covariates were demographic characteristics, lifestyle factors, and health status. Linear-by-linear association was used to assess the trends in regard to social isolation over time under the influence of the potential covariates. Linear-by-linear association and an age-period-cohort analysis were used to explore the trends, and two-level (time, individual) generalized estimating equation models (GEE) linked multivariate binary logistic regression were performed to identify risk factors. Results A high prevalence of social isolation and a moderate upward trend from 2013 to 2018 were observed among a U-shaped trend prevalence of social isolation from 2011 to 2018 across China, with rates of 38.09% (95% CI = 36.73–39.45) in 2011, 33.66% (32.32–35.00) in 2013, 39.13% (37.59–40.67) in 2015, and 39.95% (38.59–41.31) in 2018 ( p < 0.001). The prevalence of social isolation increased with age and educational attainment. Females had a higher prevalence than males. The prevalence of social isolation was found to be significantly lower in pensioners than in non-pensioners between 2011 and 2018 ( p < 0.001). The prevalence of social isolation was 38.9%, 34.9%, 38.5%, and 44.08% about three times higher among those who doid not use the Internet and 13.44%, 11.64%, 12.93%, and 16.73% than among those who doid in 2011, 2013, 2015 and 2018 respectively. The participants with short (0–5 h) and long sleep (9 or more hours), and poor self-rated health had a higher prevalence of social isolation than the others. Older age, lower educational attainment, living in a rural region, lack of medical insurance or pension, lack of internet use and poor health were risk factors ( p < 0.05). Conclusions We found a U-shaped prevalence of social isolation trends from 2011 to 2018 and revealed increasing trends from 2013 to 2018 among middle-aged and older adults in China. The findings of the study highlight the urgent need for interventions to reduce social isolation including improving sleep quality and internet skills. Disadvantaged groups in terms of age, economic status, and health status should be the focus of such interventions, especially in the era of COVID-19.

We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.

Background Urban community health services are key to promoting the high-quality development of community health. However, previous studies have seldom explored the evolutionary logic and development trend of community health service policies. It is difficult to provide a comprehensive answer to the questions of the generation, evolution and trend of community health service policies in China. Objective To understand the current status, evolutionary logic and trend of community health service policies, in order to provide intellectual reference for promoting the high-quality development of community health services and implementing the hierarchical diagnosis and treatment system. Methods The Central People's Government website, National Health Commission, relevant official provincial websites, CNKI, China Community Health Association and other platforms were searched from December 2019 to March 2022 for community health service reform related policies published at national level (n=98) from January 1997 to March 2022. The included policies were analyzed with the help of the policy orientation analysis model. Results The policy changes in urban community health services of China have gone through four stages since 1997, including initial exploration centered on the transformation and frame construction (from 1997 to 2002), normative construction focusing on the bottom of the public health network (from 2003 to 2008), prosperous development focusing on the mechanism reform (from 2009 to 2016), and deepening reform centered on quality improvement and empowerment (from 2017 to 2022). The changes in community health service policies in China follows the following evolutionary logic, including the dynamic mechanism from marketization to professionalization and social community linkage governance, target orientation from scale expansion to internal quality improvement, policy discourse changing from predominantly economics-based discourse to multiple tools coordination. Conclusion Community health service policies should promote the innovation of the dual collaborative governance framework and mechanism, strengthen the coordination among professional systems and their effective synergistic linkage with the social community governance systems; promote community value-based health care and trust-based health care with health as the core, establish and improve evaluation standards for the capacity and quality of specialized primary care; promote the diversified application and matching of policy tools to adapt to the diversified needs of community health and wellness interests.

通过文献综述和专家咨询的方法构建家庭医生获得感概念框架和评价模型.研究将家庭医生获得感界定为在家庭医生签约服务制度下,家庭医生基于客观获得而产生的主观情绪体验和情感反应的综合感受,构建以获得内容、获得体验、获得途径、获得环境、获得共享五个维度的家庭医生获得感概念框架,并结合相关获得感研究编制家庭医生获得感评价模型,包括 5 个一级指标、13 个二级指标、39 个三级指标,为家庭医生获得感研究提供参考.

Objective:To analyze the effect of the implementation of diagnosis-intervention packet (DIP) on the doctors′ diagnosis and treatment behavior of chronic diseases, so as to provide reference for further improving medical insurance payment related policies.Methods:The first page information of chronic disease patients admitted to hospitals with diabetes, hypertension and coronary atherosclerotic heart disease as the main conditions in 103 hospitals at all levels and township health centers in a city from 2016 to 2020 was collected, and the patients were divided into non-DIP group and DIP group according to the implementation time of DIP. After 1∶1 propensity score matching to balance the general conditions of the 2 groups, the diagnosis and treatment behaviors were analyzed from two dimensions: diagnostic behavior and treatment behavior. The grade A rate of medical record writing, admission and discharge diagnosis coincidence rate, and the average length of stay were used to evaluate the diagnostic behavior; the proportion of drugs and the degree of change in the cost structure were used as the evaluation indicators of treatment behavior.Results:After matching, 41 050 patients were included in both the non-DIP group and the DIP group.From the perspective of diagnostic behavior, the grade A rate of medical record writing in the non-DIP group and the DIP group was 99.40% and 99.83%, the coincidence rate of admission and discharge diagnosis was 58.42% and 61.79%, the average hospital stay was 8.03 days and 7.04 days respectively, and the difference between the groups was significant ( P<0.05). From the view of treatment behavior, the proportion of drugs decreased from 33.00% in the non-DIP group to 27.59% in the DIP group, with a significant difference ( P<0.05); the drug cost represented by Western medicine changed negatively, while the diagnostic cost showed a positive change. Conclusions:DIP has played a certain role in regulating doctors′ diagnosis and treatment behavior for chronic diseases. Among them, doctors have significantly improved their diagnostic behavior for chronic diseases, and the proportion of drugs in treatment behavior has been well controlled.

目的 探索家庭医生签约居民的初级卫生服务连续性现状及其影响因素,为改善卫生服务连续性提供参考依据.方法 采用典型抽样方法,选取东莞市两所社区卫生服务中心528名家庭医生签约居民作为研究对象,采用《基层卫生评估工具(PCAT-AS)》《家庭医生签约服务利用情况调查问卷》对其卫生服务连续性、家庭医生签约服务利用情况进行调查,并采用多元线性回归分析卫生服务连续性的影响因素.结果 共回收有效问卷528份,家庭医生签约服务利用得分为(6.32±1.50)分,服务连续性得分为(2.49±0.61)分.多元线性回归分析结果显示,文化程度、家庭人均月收入、是否患有慢性病、家庭医生签约服务利用情况对服务连续性产生影响(P<0.05).硕士及以上文化程度的居民服务连续性低于小学及以下的居民(β=-0.382);家庭人均月收入为3000～<5000元(β=-0.365)、5000～<10000元(β=-0.325)、10000～<20000元(β=-0.362)、≥20000元(β=-0.334)的居民服务连续性低于<3000元的居民;患有慢性病的患者服务连续性高于无慢性病的患者(β=0.117);家庭医生签约服务利用得分越高,服务连续性越好(β=0.137).结论 居民的卫生服务连续性有待进一步提高,文化程度、家庭人均月收入、是否患有慢性病、家庭医生签约服务利用是其重要影响因素,提高家庭医生签约服务的利用程度可以提高服务连续性.

目的 探讨湛江市5种血源及性传播疾病的发病特征,建立求和自回归移动平均(autoregressive integrated moving average,ARIMA)模型预测其月发病率,为制定防控策略提供依据.方法 收集湛江市2005-2019年5种血源及性传播疾病的报告病例数,分析其流行特征,并为每种疾病构建ARIMA模型.结果 湛江市2005-2019年累计报告血源及性传播疾病154 477例,年均发病率为148.54/10万,发病率呈长期上升趋势,男性发病数多于女性,主要发生在20～＜40岁年龄段,高发人群为农民,好发于廉江市,2月呈发病低谷期.乙型病毒性肝炎(乙肝)、丙型病毒性肝炎(丙肝)、HIV/AIDS、淋病最优模型均为ARIMA(0,1,1)(0,1,1)12,梅毒最优模型为ARIMA(0,1,1)(1,1,1)12,2019年拟合值与真实值的平均绝对百分比误差(mean absolute percentage error,MAPE)分别为 7.76％、7.58％、9.39％、19.60％、11.48％.结论 2005-2019 年湛江市血源及性传播疾病有较高的发病率,且发病有明显的地区、人群分布特点;ARIMA模型在血源及性传播疾病中有较好的预测性能,可通过模型进行短期预测,为合理配置防控资源提供依据.

[目的]构建医保基金监管第三方服务绩效评价指标体系,更好地评价、规范和监管第三方服务,有效推进医保治理现代化发展.[方法]以医保基金监管第三方服务"结构-投入-过程-产出-结果"绩效评价模型为基础,通过专家咨询法和层次分析法确定指标内容和权重,建立评价指标体系并在湛江市进行实测.[结果]构建了包括组织结构与管理(权重0.20)、资源投入(权重0.10)、监管过程(权重0.44)、监管效果(权重0.26)4 个维度的医保基金监管第三方服务绩效评价体系.[结论]指标体系的构建科学合理,以政策目标为导向、过程结果为重点,能够全过程多维度评价基金监管第三方服务.

以DRG/DIP为主导的付费方式是当前支付方式改革的必然趋势,其西医诊断与诊疗体系的核心机制与中医药诊治特点缺乏适配,导致中医药缺乏医保基金补偿的长效机制.针对医保支付与中医药的非对称问题,创新性地引入未充分补偿与非对称共摊的理念,探索适用当前付费制度改革背景下的中医药非对称共摊支持机制,并以此理念指导全面实施DRG且有深厚中医药传统的江苏省常州市,对其中医医疗机构开展非对称共摊相应的系列政策.政策实施后,常州市各中医医疗机构的支付率提升、人均住院费用下降、中医诊疗价值得到有效补偿,达到预期目标,为DRG/DIP付费制度改革背景下有效落实支持中医药可持续发展提供了机制性、系统性卫生政策支撑.

中医药在治疗慢性病、预防疾病、促进康复等方面具有独特优势,合理的医保付费制度可促进中医药发展.目前的按项目付费、DRG/DIP付费等医保支付方式具有显著的现代疾病分类与临床生化、物理及病理诊断特点,而中医临床讲究四诊合参与辨证论治,强调整体观,缺少具有标识性的疗效指标及相应标准,这些差异是医保支付方式改革需要考虑的.因此,本文提出以参保人健康为导向,遵循中医药自身发展的规律和特点,探索建立按价值付费的医保支付体系,充分发挥中医药"简、便、验、廉"和辨证施治的优势,为医保支付方式改革更好地促进中医药发展提供思考.