R.H. Ellaway is professor, Department of Community Health Sciences, and director, Office of Health and Medical Education Scholarship, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. D. Topps is professor, Department of Family Medicine, and medical director, Office of Health and Medical Education Scholarship, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. M. Pusic is associate professor of emergency medicine, Department of Emergency Medicine, and director, Division of Learning Analytics, Institute for Innovations in Medical Education, New York University School of Medicine, New York, New York.
Acad Med. 2019 Jan;94(1):31-36. doi: 10.1097/ACM.0000000000002465.
The collection and analysis of data are central to medical education and medical education scholarship. Although the technical ability to collect more data, and medical education's dependence on data, have never been greater, it is getting harder for medical schools and educational scholars to collect and use data, particularly in terms of the regulations, security issues, and growing reluctance of learners and others to participate in data collection activities. These two countervailing trends present a growing threat to the viability of medical education scholarship. In response, there must either be a more conducive data environment for medical education scholarship or medical education must move to become less dependent on data.There is, therefore, a growing need for a system-wide correction: a shift in practice that makes data use more viable and productive while maintaining high professional standards. There are five core areas that can contribute to a system-wide correction: greater clarity over what can be used as data; greater clarity on what constitutes "good" data; changes to the ways in which data are collected; better strategic stewardship of existing data; and deliberate and strategic attention to "data readiness" in support of medical education and medical education scholarship. These solutions are primarily practical and conceptual changes in the face of what are mainly regulatory challenges. However, medical educators also need to engage with emerging areas of practice such as learning analytics, and they need to consider the shifting social contract for using data in medical education.
数据的收集和分析是医学教育和医学教育研究的核心。尽管现在医学领域比以往任何时候都更有能力收集更多的数据，也更加依赖数据，但医学院和教育学者却越来越难以收集和使用数据，尤其是在法规、安全问题以及学习者和其他人员越来越不愿意参与数据收集活动方面。这两种相互矛盾的趋势对医学教育研究的可行性构成了越来越大的威胁。因此，必须为医学教育研究创造一个更有利的数据环境，或者医学教育必须减少对数据的依赖。因此，我们越来越需要进行系统范围的纠正：这需要改变实践，在保持高标准的同时，使数据的使用更加可行和富有成效。有五个核心领域可以为系统范围的纠正做出贡献：更明确地界定什么可以作为数据；更明确地界定什么构成“好”数据；改变数据收集的方式；更好地管理现有数据；以及精心策划和战略性地关注“数据准备”，以支持医学教育和医学教育研究。这些解决方案主要是针对主要的监管挑战，在实践和概念上进行的改变。然而，医学教育工作者还需要参与学习分析等新兴实践领域，并考虑在医学教育中使用数据的不断变化的社会契约。
