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The State of Data in Healthcare: Path Towards Standardization

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Abstract

Coupled with the rise of data science and machine learning, the increasing availability of digitized health and wellness data has provided an exciting opportunity for complex analyses of problems throughout the healthcare domain. Whereas many early works focused on a particular aspect of patient care, often drawing on data from a specific clinical or administrative source, it has become clear such a single-source approach is insufficient to capture the complexity of the human condition. Instead, adequately modeling health and wellness problems requires the ability to draw upon data spanning multiple facets of an individual’s biology, their care, and the social aspects of their life. Although such an awareness has greatly expanded the breadth of health and wellness data collected, the diverse array of data sources and intended uses often leave researchers and practitioners with a scattered and fragmented view of any particular patient. As a result, there exists a clear need to catalogue and organize the range of healthcare data available for analysis. This work represents an effort at developing such an organization, presenting a patient-centric framework deemed the Healthcare Data Spectrum (HDS). Comprised of six layers, the HDS begins with the innermost micro-level omics and macro-level demographic data that directly characterize a patient, and extends at its outermost to aggregate population-level data derived from attributes of care for each individual patient. For each level of the HDS, this manuscript will examine the specific types of constituent data, provide examples of how the data aid in a broad set of research problems, and identify the primary terminology and standards used to describe the data.

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Notes

  1. https://www.genome.gov/sequencingcosts/

  2. http://www.hl7.org

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This work is supported in part by the National Science Foundation (NSF) Grant IIS-1447795.

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  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46656, USA

    Keith Feldman, Reid A. Johnson & Nitesh V. Chawla

  2. iCeNSA, University of Notre Dame, Notre Dame, IN, 46656, USA

    Keith Feldman, Reid A. Johnson & Nitesh V. Chawla

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Feldman, K., Johnson, R.A. & Chawla, N.V. The State of Data in Healthcare: Path Towards Standardization. J Healthc Inform Res 2, 248–271 (2018). https://doi.org/10.1007/s41666-018-0019-8

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