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| Dublin Core |
PKP Metadata Items |
Metadata for this Document |
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| 1. |
Title |
Title of document |
Advancing Design Approaches through Data-Driven Techniques: Patient Community Journey Mapping Using Online Stories and Machine Learning |
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| 2. |
Creator |
Author's name, affiliation, country |
Jiwon Jung; Delft University of Technology (TU Delft), Delft, the Netherlands / Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; |
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| 2. |
Creator |
Author's name, affiliation, country |
Ki-Hun Kim; Delft University of Technology (TU Delft), Delft, the Netherlands / Pusan National University, Busan, South Korea; Korea, Republic Of |
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| 2. |
Creator |
Author's name, affiliation, country |
Tess Peters; Delft University of Technology (TU Delft), Delft, the Netherlands; Netherlands |
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| 2. |
Creator |
Author's name, affiliation, country |
Dirk Snelders; Delft University of Technology (TU Delft), Delft, the Netherlands; Netherlands |
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| 2. |
Creator |
Author's name, affiliation, country |
Maaike Kleinsmann; Delft University of Technology (TU Delft), Delft, the Netherlands / Leiden University Medical Center (LUMC), Leiden, the Netherlands; Netherlands |
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| 3. |
Subject |
Discipline(s) |
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| 3. |
Subject |
Keyword(s) |
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| 4. |
Description |
Abstract |
Designers are increasingly collaborating with data scientists to apply smart data technologies to understand large-scale user behavior during their design research. This is useful in specific impact domains with vulnerable users and unfamiliar contexts, such as healthcare design. Patient journey mapping is the most common design tool for developing and communicating patient-centred perspectives in healthcare design. However, creating a traditional patient journey map is labor intensive. Consequently, they often represent the experiences of a limited number of patients and, therefore, have limitations in including an extensive group patient experience. To overcome these challenges, we present a new data-driven and hybrid intelligent design approach that utilizes tens of thousands of online patient stories and machine-learning techniques through collaboration with data scientists. We set up two studies in the field of oncology and demonstrate that combining the two machine-learning techniques allows for quantifying the experiences of a wide range of patients, detecting relationships between co-occurring experiences within the journey, and detecting new design opportunities/directions. In these studies, designers gained a large-scale, yet qualitative and inspiring, understanding of a complex context in healthcare with reduced time and cost investments. |
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| 5. |
Publisher |
Organizing agency, location |
Chinese Institute of Design |
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| 6. |
Contributor |
Sponsor(s) |
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| 7. |
Date |
(YYYY-MM-DD) |
2023-08-30 |
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| 8. |
Type |
Status & genre |
Peer-reviewed Article |
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| 8. |
Type |
Type |
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| 9. |
Format |
File format |
PDF, HTML |
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| 10. |
Identifier |
Universal Resource Indicator |
https://www.ijdesign.org/index.php/IJDesign/article/view/4671 |
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| 11. |
Source |
Journal/conference title; vol., no. (year) |
International Journal of Design; Vol 17, No 2 (2023) |
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| 12. |
Language |
English=en |
en |
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| 14. |
Coverage |
Geo-spatial location, chronological period, research sample (gender, age, etc.) |
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| 15. |
Rights |
Copyright and permissions |
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