Author Affiliations: School of Nursing (Dr Pruinelli, Ms Fesenmaier, and Dr Delaney) and Institute for Health Informatics (Dr Johnson), University of Minnesota, Minneapolis, Minnesota; School of Medicine, University of Kansas Medical Center (Dr Winden), Kansas City; and Kirkhof College of Nursing, Grand Valley State University (Dr Coviak), Grand Rapids, Michigan.
Comput Inform Nurs. 2020 Oct;38(10):484-489. doi: 10.1097/CIN.0000000000000607.
Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.
护理领导者在处理大量跨学科医疗保健数据时，需要先进的分析指导来改善人群的健康结果。本文报告了制定路线图的过程，以帮助护理领导者使用数据科学原则和工具来做出决策，从而支持改善所有人医疗保健的临床实践研究和方法。该路线图的开发基于大数据科学的跨行业标准流程，采用了共识构建和迭代的过程。使用该模型，描述了一组组件，这些组件结合并实现了适用于医疗保健问题的数据科学项目的流程，具有改善人群健康结果的潜力。路线图通过研讨会的形式进行了测试。研讨会面向两个受众群体：护理领导者和信息学/医疗保健领导者。结果是积极的，包括进一步完善和传达路线图的建议。
