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Clinical Informatics Study Guide

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Abstract

Increasing accessibility and availability of heterogeneous data sources, growing data science competencies across the clinical workforce, and a preponderance of evidence on the value and benefits of Artificial Intelligence (AI) present a rosy future for analytics in healthcare. However, navigating the complex, computer science-heavy analytics domain and translating these concepts for application in clinical care is no easy task. This chapter introduces basic concepts of data and analytics for the clinical informatics domain and practical considerations, challenges, and solutions that may influence clinical informaticians in applying these methods in practice.

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Author information

Authors and Affiliations

  1. Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA

    Suranga N. Kasthurirathne & Shaun J. Grannis

  2. Indiana University School of Medicine, Indianapolis, USA

    Suranga N. Kasthurirathne & Shaun J. Grannis

Authors
  1. Suranga N. Kasthurirathne
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  2. Shaun J. Grannis
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Corresponding author

Correspondence to Suranga N. Kasthurirathne .

Editor information

Editors and Affiliations

  1. Indiana University School of Medicine, Indianapolis, IN, USA

    John T. Finnell

  2. Regenstrief Institute, Indianapolis, IN, USA

    Brian E. Dixon

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Kasthurirathne, S.N., Grannis, S.J. (2022). Analytics. In: Finnell, J.T., Dixon, B.E. (eds) Clinical Informatics Study Guide. Springer, Cham. https://doi.org/10.1007/978-3-030-93765-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-93765-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93764-5

  • Online ISBN: 978-3-030-93765-2

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