Abstract
The increasing availability of electronic health care records has provided remarkable progress in the field of population health. In particular the identification of disease risk factors has flourished under the surge of available data. Researchers can now access patient data across a broad range of demographics and geographic locations. Utilizing this Big healthcare data researchers have been able to empirically identify specific high-risk conditions found within differing populations. However to date the majority of studies approached the issue from the top down, focusing on the prevalence of specific diseases within a population. Through our work we demonstrate the power of addressing this issue bottom-up by identifying specifically which diseases are higher-risk for a specific population. In this work we demonstrate that network-based analysis can present a foundation to identify pairs of diagnoses that differentiate across population segments. We provide a case study highlighting differences between high and low income individuals in the United States. This work is particularly valuable when addressing population health management within resource-constrained environments such as community health programs where it can be used to provide insight and resource planning into targeted care for the population served.
Highlights
Today we are witnessing a shift in the landscape of modern healthcare
Those studies that do attempt to address the issue at a population level have done so with respect to specific diseases such as diabetes, cardiovascular disease and mental disorders[6,7,8]
The evaluation of the network-based technique proposed in this work was broken down into two distinct analysis, each of which investigated a different aspect of the diagnosis variations between the population subgroups
Summary
The rapid emergence and adoption of Electronic Medical Records (EMR) has led to a sundry of analytic technologies These technologies utilize aggregated EMR’s from numerous individuals in conjunction with machine learning and statistical techniques to provide personalized diagnoses based on a patient’s specific health conditions, clinical decision support systems, and numerous other tools employing secondary uses of EMR data[1,2,3,4,5]. Since the publication of these works, there have been a number of additional studies investigating the relation of income to health more closely[12,13,14] While these studies have provided a detailed evaluation into how socioeconomics can influence a population’s health, they fail to address the major factor of identifying which diseases the population is at risk for
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