Abstract
In this paper, we draw inspiration from the Discordant Chronic Comorbidity Care (DC\(^3\)) model. The model recognizes the complexities of DCCs and incorporates key strategies for assessing and addressing the complexities of DCCs care. We worked with user experience design experts over several design sprints to come up with a conceptual design. It became clear early on that because of the changing DCCs care needs, there is no one-size-fits-all solution for DCCs needs. Thus, the effective care of DCCs requires a holistic approach. The holistic approach involves designers collecting multiple individual tools and mapping those tools to specific needs for DCC care and treatment, which ultimately results in the creation of an ecosystem. We discussed how this ecosystem may be optimized and personalized using machine learning to address individual DCCs needs. Furthermore, putting together these multiple sets of tools could introduce an engineering challenge. We provide strategies and recommendations for future work to address these engineering challenges and how to make a theoretical concept adaptable to technology.
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Acknowledgement
It is with true pleasure that we acknowledge the contributions of our design experts Daria Loi, Kevin Shaw, Timothy Kelly, Janette Shew, and Ginny Hong. Thank you for generously sharing your time, experience, and other resources for this project.
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Ongwere, T., Stolterman, E., Shih, P.C., James, C., Connelly, K. (2022). Translating a DC\(^3\) Model into a Conceptual Tool (DCCs Ecosystem): A Case Study with a Design Team. In: Lewy, H., Barkan, R. (eds) Pervasive Computing Technologies for Healthcare. PH 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 431. Springer, Cham. https://doi.org/10.1007/978-3-030-99194-4_24
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