Continuing medical education in China: evidence from primary health workers' preferences for continuing traditional Chinese medicine education.

BACKGROUND: Continuing Medical Education (CME) is an important part of the training process for health workers worldwide. In China, training in Traditional Chinese Medicine (TCM) not only improves the expertise of medical workers, but also supports the Chinese Government's policy of promoting TCM as an equal treatment to western medicine. CME, including learning Traditional Chinese Medicine Technologies (TCMTs), perform poorly and research into the motivation of health workers to engage in CME is urgently required. Using a discrete choice experiment, this study assessed the CME learning preferences of primary health workers, using TCMT as a case study of CME programs. METHODS: We conducted a discrete choice experiment among health workers in Shandong Province, Guizhou Province, and Henan provinces from July 1, 2021 to October 1, 2022 on the TCMT learning preferences of primary health workers. The mixed logit model and latent class analysis model were used to analyze primary health workers' TCMT learning preferences. RESULTS: A total of 1,063 respondents participated in this study, of which 1,001 (94.2%) passed the consistency test and formed the final sample. Our key finding was that there were three distinct classes of TCMT learners. Overall, the relative importance of the seven attributes impacting the learning of TCMTs were: learning expenses, expected TCMT efficacy, TCMT learning difficulty, TCMT mode of learning, TCMT type, time required to learn, and expected frequency of TCMT use. However, these attributes differed significantly across the three distinct classes of TCMT learners. Infrequent users (class 1) were concerned with learning expenses and learning difficulty; workaholics (class 2) focused on the mode of learning; and pragmatists (class 3) paid more attention to the expected TCMT efficacy and the expected frequency of TCMT use. We recommend targeted strategies to motivate TCMT learning suited to the requirements of each class of TCMT learners. CONCLUSION: Rather than a single TCMT medical education program for primary health workers, CME programs should be targeted at different classes of TCMT learners.

Who consumes curative care expenditure of medical institutions in Beijing: a case study based on System of Health Accounts 2011.

BACKGROUND: China's health system is challenged by complex health problems experienced by different population groups and caused by multiple diseases. This study examined the distribution of curative care expenditure (CCE) of medical institutions in Beijing using beneficiary characteristics such as residency, gender, age, and disease. Suggestions are presented for the development of health policies. METHODS: A total of 81 medical institutions with approximately 80 million patients in Beijing, China, were selected via a multistage stratified cluster random sampling approach. Based on this sample, the System of Health Accounts 2011 was used to estimate the CCE of medical institutions. RESULTS: The CCE of medical institutions in Beijing was ¥246.93 billion in 2019. The consumption of patients from other provinces was ¥60.04 billion, accounting for 24.13% of the total CCE. The CCE of female consumption (52.01%/¥128.42 billion) exceeded that of male consumption (47.99%/¥118.51 billion). Almost half of the CCE (45.62%/¥112.64 billion) was consumed by patients aged 60 or above. Adolescent patients up to an age of 14 (including those aged 14) mainly chose secondary or tertiary hospitals for treatment. Chronic non-communicable diseases accounted for the largest share of CCE consumption, with circulatory diseases accounting for the highest proportion. CONCLUSIONS: This study identified significant differences in CCE consumption in Beijing according to region, gender, age, and disease. Currently, the utilization of resources in medical institutions is not reasonable, and the hierarchical medical system is not sufficiently effective. Therefore, the government needs to optimize the allocation of resources according to the needs of different groups and rationalize the institutional process and functions.