[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122780-en":3,"doc-seo-122780-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122780,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Advancing Design Approaches through Data-Driven Techniques - Patient Community Journey Mapping Using Online Stories and Machine Learning","Designers increasingly partner with data scientists to apply intelligent, data-driven methods for understanding large-scale user behavior, especially in complex domains like healthcare where contexts are unfamiliar and users are vulnerable. Traditional patient journey mapping is labor intensive and often relies on limited patient samples, restricting coverage of community experiences. This work proposes a data-driven hybrid approach using tens of thousands of online patient stories and machine-learning techniques. Two oncology studies show the method quantifies diverse experiences, identifies relationships among co-occurring events, and surfaces new design opportunities, enabling large-scale qualitative insights with reduced time and cost.","ORIGINAL ARTICLE    \nAdvancing Design Approaches through Data-Driven Techniques: Patient Community Journey Mapping Using Online Stories and Machine Learning  \nJiwon Jung *,1,2, Ki-Hun Kim 1,3, Tess Peters 1, Dirk Snelders 1, and Maaike Kleinsmann 1,4  \n1 Delft University of Technology (TU Delft), Delft, the Netherlands  \n2 Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands  \n3 Pusan National University, Busan, South Korea  \n4 Leiden University Medical Center (LUMC), Leiden, the Netherlands  \nDesigners 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 ofa 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.  \nKeywords – Patient Journey Mapping, Machine Learning, Hybrid Intelligence, Patient Stories, Healthcare Design.  \nRelevance to Design Practice –Analyzing the experiences oftens of thousands of patients supports designers to understand and respond to an extensive and collective user group experience at a level that, previously, was practically unfeasible. Hence, we showcase the potential of collaborations between designers and data scientists, both within and beyond healthcare design.  \nCitation: Jung, J., Kim, K., Peter, T., Snelders, D., & Kleinsmann, M. (2023). Advancing design approaches through data-driven techniques: Patient community journey mapping  \nusing online stories and machine learning. International Journal of Design, 17(2), 19-44. [https://doi.org/10.57698/v17i2.02](https://doi.org/10.57698/v17i2.02)  \nIntroduction  \nDesign research is currently exploring collaborations with data scientists. It is to develop new data-driven design approaches for understanding user needs and prototyping design outcomes by using large-scale data and analyzing them using smart technologies such as machine learning (Bourgeois & Kleinsmann, 2023; Cooper, 2019; Giaccardi et al., 2016; Speed & Oberlander, 2016; Verganti et al., 2020) . This new development builds on an emerging need for designers who work more and more on societal challenges. Moreover, this type of collaboration buildson a rich tradition in design (Andreasen, 2011; Cross, 2018) in improving design research and practice through innovations in technology-driven design approaches. Such data-driven design approach through data scientist collaboration is a hybrid intelligence approach, that uses the complementary strengths of human (in our case, designers, and data scientists) and machine intelligence (Dellermann et al., 2019; Kamar, 2016), that uses machine learning to analyze a large scale user dataset, and uses human’s competences to interpret the analysis outcome.  \nHealthcare, one of societal challenges that designers work on, is an impactful domain to explore such data-driven design approach (Tsekleves & Cooper","cbCaiucnXNhRKFCL","https://ap.wps.com/l/cbCaiucnXNhRKFCL","pdf",6425003,1,26,"English","en",105,"# Introduction\n## Data-driven design and hybrid intelligence\n## Healthcare design challenges\n## Patient journey mapping and limitations\n## Article scope and objectives","[{\"question\":\"What problem does the approach address in patient journey mapping?\",\"answer\":\"Traditional patient journey mapping requires intensive labor and often represents only a limited number of patients, limiting the ability to capture broad community experiences.\"},{\"question\":\"What data and techniques are used in the proposed method?\",\"answer\":\"The method leverages tens of thousands of online patient stories and applies machine-learning techniques, developed through collaboration with data scientists.\"},{\"question\":\"What outcomes were demonstrated in the oncology studies?\",\"answer\":\"The approach quantifies experiences across a wide range of patients, detects relationships between co-occurring experiences in the journey, and identifies new design opportunities or directions.\"}]","Advancing Design Approaches through Data-Driven Techniques - Patient Community Journey Mapping Using Online Stories and Machine Learning | PDF",1785812860,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advancing-design-approaches-through-data-driven-techniques-patient-community-journey-mapping-using-online-stories-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advancing-design-approaches-through-data-driven-techniques-patient-community-journey-mapping-using-online-stories-and-machine-learning/122780/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the approach address in patient journey mapping?","Question",{"text":75,"@type":76},"Traditional patient journey mapping requires intensive labor and often represents only a limited number of patients, limiting the ability to capture broad community experiences.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and techniques are used in the proposed method?",{"text":80,"@type":76},"The method leverages tens of thousands of online patient stories and applies machine-learning techniques, developed through collaboration with data scientists.",{"name":82,"@type":73,"acceptedAnswer":83},"What outcomes were demonstrated in the oncology studies?",{"text":84,"@type":76},"The approach quantifies experiences across a wide range of patients, detects relationships between co-occurring experiences in the journey, and identifies new design opportunities or directions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]