结合劳动力数据对医院入院数据进行探索性多变量分析。

Exploratory multivariate analysis of hospital admissions data in conjunction with workforce data.

作者信息

Good Norm, Khanna Sankalp, Boyle Justin

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2626-2629. doi: 10.1109/EMBC.2017.8037396.

DOI:10.1109/EMBC.2017.8037396

Abstract

The prevalence of electronic health data has brought us a step closer to understanding of the dynamics of hospital admissions. However, little research has investigated hospital admission data in conjunction with information about the environment where the patient was admitted, such as staffing level and hospital type. This paper studied this crucial but often neglected issue by investigating hospital admission records together with workforce data. Exploratory multivariate analysis methods, such as principal component analysis (PCA) and multiple correspondence analysis (MCA), were applied to study important variables associated with admission and workforce data. The exploratory results obtained shed light on the contribution of these variables to the typology of hospital admissions.

摘要

电子健康数据的普及让我们在了解医院入院动态方面又迈进了一步。然而，很少有研究将医院入院数据与患者入院环境的信息（如人员配备水平和医院类型）结合起来进行调查。本文通过对医院入院记录和劳动力数据进行调查，研究了这个关键但常常被忽视的问题。探索性多变量分析方法，如主成分分析（PCA）和多重对应分析（MCA），被用于研究与入院和劳动力数据相关的重要变量。所获得的探索性结果揭示了这些变量对医院入院类型的贡献。