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Advanced Data Visualisation in Health Economics and Outcomes Research: Opportunities and Challenges

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Abstract

Data visualisation techniques are valuable tools for exploring, synthesising and communicating the results of research studies. Advanced data visualisation techniques, including dynamic and interactive visualisations, are just beginning to be used in health economics and outcome research (HEOR). In HEOR, there is the potential to use these techniques both to explore methodological challenges that are central to the design and interpretation of the findings of pharmacoeconomic and outcomes research studies, but also to communicate research findings to various stakeholders. In this manuscript, we discuss opportunities and methodological challenges for data visualisation specific to HEOR, describe external barriers that may impact the use of data visualisation methods, and discuss future applications of this technology in HEOR. While there are a number of obvious applications within the data-heavy field of HEOR, caution is required to ensure that visualisations, particularly advanced ones, accurately and fairly reflect the underlying data. However, researchers will benefit from adopting these increasingly sophisticated techniques to help ensure that decisionmakers and other stakeholders can understand, digest and communicate the data—which is critical for achieving the ultimate goal of improving patient outcomes.

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Modified from Yau et al. [18]

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Author information

Authors and Affiliations

  1. Broadstreet HEOR, Vancouver, BC, Canada

    Shelagh M. Szabo & Karissa M. Johnston

  2. AcasterLloyd Consulting, London, UK

    Andrew J. Lloyd

Authors
  1. Shelagh M. Szabo
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  2. Karissa M. Johnston
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  3. Andrew J. Lloyd
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Contributions

AJL and SMS were primarily responsible for drafting the manuscript, and all authors critically reviewed and edited the content. Participation in development of the visualisations was as follows: Pharmacist model: KMJ. Hodgkin lymphoma visualisation: KMJ, SMS. NMA visualisation: KMJ, SMS. Static Sankey plot: KMJ, SMS. Dynamic Sankey plot: AJL, SMS. Bivariate area chart: KMJ, AJL, SMS.

Corresponding author

Correspondence to Shelagh M. Szabo.

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Conflict of interest

SMS: none; KMJ: none; AJL: none.

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None.

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Szabo, S.M., Johnston, K.M. & Lloyd, A.J. Advanced Data Visualisation in Health Economics and Outcomes Research: Opportunities and Challenges. Appl Health Econ Health Policy 17, 433–441 (2019). https://doi.org/10.1007/s40258-019-00476-5

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  • DOI: https://doi.org/10.1007/s40258-019-00476-5

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