19 May 2016 Analysis of geographical variations of healthcare providers performance using the empirical mode decomposition
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Abstract
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.
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Michael A. Pratt, Michael A. Pratt, Henry Chu, Henry Chu, } "Analysis of geographical variations of healthcare providers performance using the empirical mode decomposition", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710S (19 May 2016); doi: 10.1117/12.2228917; https://doi.org/10.1117/12.2228917
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