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

机译：经济模式分解的医疗保健提供者性能的地理变化分析

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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：医疗保健提供者等医疗提供者的表现因其他地区而异。我们的目标是研究使用超过3,000多个医院分布在美国的医院的指标是否存在地理模式。经验模式分解（EMD）是非线性和非静止信号的有效分析工具。它将数据序列分解为一系列内在模式功能（IMF）以及代表趋势的残差序列。每个IMF都有零本地均值，并且在任何两个连续的本地极值之间都有一个零横向。可用于评估瞬时频率的IMF。使用残留物的重建信号和较低频率的那些IMF可以揭示信号的底层图案，而不会对数据的较高频率波动的过度影响。我们使用了一个空间填充曲线来将一组性能指标逆转到二维平面表面中不规则地分布到一维序列。 EMD将一套医院应急部门中位数等待时间分解为9个IMF以及残留物。我们使用残留物和较低频率的IMF来重建具有较少波动的序列。将该序列转换回二维图以揭示地理变异。

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