Abstract

Genetic and healthcare data have experienced an explosion in the last decade and with it, a deluge of new and cutting-edge research as well as digital tools and software. However, visualizing and working with such large amounts of information also poses organizational challenges in transdisciplinary collaboration between scientific domain experts and design professionals. We propose Health Information Design Model (HIDeM), a collaborative process model with a novel series of principles and activities that tackle the specific nature of digital, data-intensive products for scientific research while considering different organizational contexts. The model was developed in collaboration with several domain experts in the life sciences and has been tested in diverse scenarios. We also present a use case that demonstrates this model’s potential and outcomes.

Keywords

health informatics, design for healthcare, information design, interdisciplinary collaboration

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Conference Track

Research Paper

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Jun 25th, 9:00 AM

Health information design model (HIDEM): A replicable model of the design process for data-intensive applications in health informatics

Genetic and healthcare data have experienced an explosion in the last decade and with it, a deluge of new and cutting-edge research as well as digital tools and software. However, visualizing and working with such large amounts of information also poses organizational challenges in transdisciplinary collaboration between scientific domain experts and design professionals. We propose Health Information Design Model (HIDeM), a collaborative process model with a novel series of principles and activities that tackle the specific nature of digital, data-intensive products for scientific research while considering different organizational contexts. The model was developed in collaboration with several domain experts in the life sciences and has been tested in diverse scenarios. We also present a use case that demonstrates this model’s potential and outcomes.

 

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