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Analysis of geographical variations of healthcare providers performance using the empirical mode decomposition

机译：基于经验模态分解的医护人员绩效地域变化分析

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Performance of healthcare providers such as hospitals varies from one locale to another. Our goal is to study whether there is a geographical pattern of performance using metrics reported from over 3,000 hospitals distributed across the U.S. Empirical mode decomposition (EMD) is an effective analysis tool for nonlinear and non-stationary signals. It decomposes a data sequence into a series of intrinsic mode functions (IMFs) along with a residue sequence that represents the trend. Each IMF has zero local mean and has exactly one zero crossing between any two consecutive local extrema. An IMF can be used to assess the instantaneous frequency. Reconstruction of a signal using the residue and those IMFs of the lower frequency can reveal the underlying pattern of the signal without undue influence of the higher frequency fluctuations of the data. We used a space-filling curve to turn a set of performance metrics distributed irregularly across the two-dimensional planar surface into a one-dimensional sequence. The EMD decomposed a set of hospital emergency department median waiting times into 9 IMFs along with a residue. We used the residue and the lower frequency IMFs to reconstruct a sequence with fewer fluctuations. The sequence was transformed back to a two-dimensional map to reveal the geographical variations.
机译：诸如医院之类的医疗保健提供者的绩效因地区而异。我们的目标是使用在美国分布的3,000多家医院报告的指标来研究绩效是否存在地理格局。经验模态分解（EMD）是一种有效的非线性和非平稳信号分析工具。它将数据序列与代表趋势的残差序列分解为一系列固有模式函数（IMF）。每个IMF的局部均值为零，并且在任意两个连续的局部极值之间恰好有一个零交叉。 IMF可用于评估瞬时频率。使用残差和较低频率的那些IMF重建信号可以揭示信号的基本模式，而不会受到数据较高频率波动的不当影响。我们使用空间填充曲线将一组不规则分布在二维平面上的性能指标转换为一维序列。 EMD将一组医院急诊部门的中位等待时间连同残留物分解为9个IMF。我们使用残差和较低频率的IMF重构了波动较小的序列。该序列被转换回二维图以揭示地理差异。

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