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

Michael A. Pratt; Henry Chu

doi:10.1117/12.2228917

19 May 2016 Analysis of geographical variations of healthcare providers performance using the empirical mode decomposition

Michael A. Pratt, Henry Chu

Author Affiliations +

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 98710S (2016) https://doi.org/10.1117/12.2228917
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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 and 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); https://doi.org/10.1117/12.2228917

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KEYWORDS

Signal to noise ratio

Medicine

Signal processing

Computer vision technology

Image analysis

Interference (communication)

Nonlinear optics

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