关于美国人医疗保健使用和费用的最完整的数据来源是什么?

挑战

Policy changes in the U.S. 影响美国人如何使用和支付医疗保健服务和处方药的医疗保健交付和融资系统需要持续大规模的数据更新. 这些更新不仅预测了政府需要在联邦医疗保健项目上花费多少——现在的和将来的——还有药品福利, but also alert policymakers about pressure points in the private sector. 例如, 这些可能包括没有医疗保险的美国人比例的增加及其对急诊室服务使用的影响. 随着国家卫生保健系统的不断变化，所有这些关键数据都需要及时和持续.

通过医疗支出小组调查(MEPS)的家庭组成部分(HC)有效和准确地收集这些数据需要一个具有广泛大规模调查经验的高技能研究公司, an extensive staff of specialists, including data science experts and field data collectors, state-of-the-art technology, and multimode approaches, to efficiently tackle data collection and processing to create public-use files.

趣赢平台开展MEPS-HC是为了支持医疗保健研究和质量机构(AHRQ)的使命，即提供证据，使医疗保健更安全, 更高质量的, 公平的, 更容易, 和负担得起的.

解决方案

每年, 趣赢平台 fields a new panel of approximately 10,000 households that responded to the National Health Interview Survey in the prior year. These households are invited both to participate in a series of 5 interviews over a 2.为期5年，并允许MEPS从其医疗保健提供者的记录中获取数据. 这种设计产生了一系列重叠的面板，因此每年我们可以将来自两个面板的数据组合起来，以创建与美国相关的年度估计.S. 平民. To manage this vast amount of data, 趣赢平台 has produced 4,000类变量来估算卫生保健支出并减少MEPS支出估算中的偏差.

Our interviewers typically conduct the main interview in person, supported by computer-assisted personal interviewing (CAPI), computer-assisted telephone interviews (CATI), and computer-assisted video interviews (CAVI). 除了, 我们使用网络和纸质自我管理问卷(SAQs)来收集每个家庭中成年人的额外信息.

When the 新型冠状病毒肺炎 pandemic interrupted in-person interviews in 2020 and into 2021, we responded with creativity and flexibility, 寻求其他模式来恢复数据收集，同时又不影响估算工作的进行. 我们立即转向电话采访，并建立了一个现有的受访者网站，为家庭提供材料和“展示卡”，这样他们就可以看到特定项目的回应类别. For a new 2021 SAQ on social determinants of health, 我们制作了网页版和纸质版的问卷，并使用电子邮件和短信邀请受访者完成网页版的问卷. In 2022 we introduced CAVI for the main interviews, 使我们的采访者能够通过眼神交流和一个专门的窗口来展示展示卡来与受访者建立融洽的关系, eliminating the burden on the respondent to visit a website to see them.

MEPS-HC数据采集器配备了先进的技术来支持趣赢平台的数据收集过程. Our advanced field operating system supports case assignments, local and nationwide travel, and all time and expense reporting. These systems operate on laptops and mobile devices, and we use GIS data to assist data collectors in navigating to, and making contact with, 受访者. 该系统还收集了一套广泛的业务数据，支持快速反馈给外地工作人员, reporting to managers and clients, 质量控制, and survey methods research.

MEPS leverages data science methods among them natural language processing (NLP), machine learning (ML), and deep learning techniques to improve survey processing and imputation of missing data. 例如, our interviewers can make open-ended comments to clarify respondent answers. 关于 20,000+ comments are entered by interviewers each year into the CAPI system. 我们已经训练了一个分类模型来自动将评论标记为10个预定义的类. 这提高了处理效率，同时保持了严格的数据质量标准. 除了, throughout the data collection process, our large staff of specialists look for anomalies in the data to assure its quality. As part of our 质量控制, our data science unit, using artificial intelligence (AI), 扫描数千份计算机辅助记录访谈(CARI)的笔录，以确定是否存在可能危及数据质量的虚假回答. 通过创新的数据科学技术来提高数据管理的效率是至关重要的，因为MEPS需要快速周转来为下一个调查周期提供案例.

我们还在与人权组织合作，为具有具体健康问题的欧洲议会议员答复者制定网络saq, 比如糖尿病, 癌症, 或者心脏病. 我们正在使用多模式和先进技术实施持续改进，以减少少报和被调查者的负担. 以这种方式, we can gather health event data more continuously, streamline the in-person interviews, 提高数据质量, and reduce perceived respondent burden.

结果

自1996年以来, we have conducted nearly 1 million in-person MEPS-HC survey interviews, 向政府提供有关美国人医疗保健服务使用和费用的年度全国估计, how they pay for them, their health insurance coverage, 健康状况, and other items related to health care usage.

每年, we produce 3 major data files and a series of topic-specific files, combining the data reported by households with data from their health care providers, and imputing for missing data. 这些数据不断地为政府提供信息，告诉他们如何最好地提高联邦医疗保健计划的质量，并扩大获得医疗保健的机会.