提升新冠疫情之外的数据监测韧性：中国基层医疗质量队列研究（ACACIA）使用未事先告知的标准化患者的经验。
Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients.
机构信息
Dong (Roman) Xu is with SMU Institute for Global Health (SIGHT), School of Health Management and Dermatology Hospital, Southern Medical University (SMU), Guangzhou, China. Yiyuan Cai is with the Department of Epidemiology and Health Statistics, School of Public Health, Guizhou Medical University, Guizhou, China. Xiaohui Wang is with the Department of Social Medicine and Health Management, School of Public Health, Lanzhou University, Lanzhou, China. Yaolong Chen is with the Institute of Health Data Science, Lanzhou University, Lanzhou, China. Wenjie Gong is with HER Team and the Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China. Jing Liao and Jinghua Li are with the Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. Jifang Zhou is with the School of International Business, China Pharmaceutical University, Nanjing, China. Zhongliang Zhou is with the School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China. Nan Zhang and Huijuan Liang are with the School of Health Management, Inner Mongolia Medical University, Hohhot, China. Chengxiang Tang is with the Macquarie University Centre for the Health Economy, Macquarie Business School, Macquarie University, Sydney, Australia. Baibing Mi is with the Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China. Yun Lu is with the Department of Preventive Medicine, Maternal and Child Health, School of Public Health, Guizhou Medical University, Guizhou, China. Ruixin Wang is with the Department of Health Economics, School of Public Health, Fudan University, Shanghai, China. Jay Pan is with HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
出版信息
Am J Public Health. 2022 Jun;112(6):913-922. doi: 10.2105/AJPH.2022.306779. Epub 2022 Apr 28.
We analyzed COVID-19 influences on the design, implementation, and validity of assessing the quality of primary health care using unannounced standardized patients (USPs) in China. Because of the pandemic, we crowdsourced our funding, removed tuberculosis from the USP case roster, adjusted common cold and asthma cases, used hybrid online-offline training for USPs, shared USPs across provinces, and strengthened ethical considerations. With those changes, we were able to conduct fieldwork despite frequent COVID-19 interruptions. Furthermore, the USP assessment tool maintained high validity in the quality checklist (criteria), USP role fidelity, checklist completion, and physician detection of USPs. Our experiences suggest that the pandemic created not only barriers but also opportunities to innovate ways to build a resilient data collection system. To build data system reliance, we recommend harnessing the power of technology for a hybrid model of remote and in-person work, learning from the sharing economy to pool strengths and optimize resources, and dedicating individual and group leadership to problem-solving and results. (. 2022;112(6):913-922. https://doi.org/10.2105/AJPH.2022.306779).
我们分析了 COVID-19 对中国使用未事先通知的标准化患者（USPs）评估基层医疗保健质量的设计、实施和有效性的影响。由于疫情，我们众筹资金，从 USP 病例清单中删除了肺结核，调整了普通感冒和哮喘病例，对 USP 进行了混合在线-线下培训，在各省之间共享 USP，并加强了伦理考虑。通过这些改变，我们能够在频繁的 COVID-19 中断的情况下开展实地工作。此外，USP 评估工具在质量清单（标准）、USP 角色保真度、清单完成和医生检测 USP 方面保持了很高的有效性。我们的经验表明，疫情不仅带来了障碍，也为创新建立有弹性的数据收集系统的方法创造了机会。为了建立对数据系统的依赖，我们建议利用技术的力量建立远程和现场工作的混合模式，从共享经济中学习，集中优势，优化资源，并投入个人和团队领导力来解决问题和取得成果。（2022；112(6):913-922。https://doi.org/10.2105/AJPH.2022.306779）。
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