Data and the transformation of design research

News - 28 August 2023

The rise of data technologies is changing the game for design researchers. As design moves towards complex societal challenges, design tools need to evolve by integrating the benefits of both humans and advanced technologies. For her PhD, Jiwon Jung explored how designers can work with data scientists and machine learning in developing digital health solutions, proposing that there is a lot to learn from each other.

Design in transition

During her design studies in Korea, Jung said she realised that the way designers did research was transforming. Putting users in a usability testing room to see how they responded to products, or observing users in their own context soon evolved into designers putting sensors in environments or products to be able to track users without interference and on a larger scale. “I started thinking that if what we are designing [i.e. from products to complex systems] evolves then the way we are designing should also evolve,” she said. That was the starting point for Jung’s PhD and she began to create a vision about how the new era of computing will impact design tasks, processes, and the designer’s role.

Collaborative solutions

Considering the development of data collection and analysis technologies, Jung set out to explore the potential future impact of design when it comes to digital health. Digital health is by nature a transdisciplinary and collaborative effort that involves fields like policy, economics, medical research, and behavioural science. Through her research, Jung wanted to show how collaborations with data-scientists could support designers in contributing to the societal transition from traditional care delivery models towards a value-based healthcare system centred around the patient’s experience.

“Whereas the data scientist can imagine the possibilities for machine learning techniques in analysing community-level data, designers can imagine and explain which data analysis results can provide high-quality information that can serve as an inspiration to the design of new services or systems,” she wrote in her thesis.

From page 29 of Jiwon's thesis: "A part of the Chapter 3 outcome, an example of Patient Community Journey Mapping (modified from (Peters, 2021))"

More data = better patient-centred healthcare

To understand patient experiences on a much larger scale, Jung created a tool called Patient Community Journey Mapping. While patient journey mapping is a commonly used design tool, it is labour intensive and typically results in limited qualitative data. Through machine learning, Jung’s tool offers a novel approach using existing databases of patient stories from online patient communities. These databases provide a source for tens of thousands of first-hand patient accounts about individual care path and illness experiences. 

Working together with Erasmus University Medical Center (Erasmus MC), Jung’s research involved digital health solutions for cardiology and cancer patients. “These people live with great uncertainties,” she said. “Using my tool, we analysed tens of thousands of patient stories, then we extracted information about patient needs at certain points of their care trajectory. We wanted to provide the right information at the right time to the patients so they could feel assured about follow up care.” This is just one example of how using this new data-enabled approach allows for a cost effective and efficient way to include community-level data in designing digital health solutions. It gives designers a better understanding of a complex context in healthcare which can ultimately help improve care pathways, products and services

Together with other research collaborators, Jung received a Convergence Open Mind Call grant and an NWO take-off phase 1 grant to create the tool during her PhD. She continues to support and advise three TU Delft graduates who are carrying the work forward through a start-up called Qaring.

New tools, new perspectives

Digital healthcare is a complex societal challenge, often beyond the individual context, and cannot be tackled by one-sided efforts, said Jung. For that reason, her research is rooted in the need to advance design methods and tools to include collective patients’ perspectives in the digital health design process. With the rise of toolkits and services to easily exploit technologies like artificial intelligence and machine learning, designers now have powerful tools at their disposal. But Jung emphasises that such technologies should be viewed as enabling tools, part of a bigger picture.

They can help designers understand things from a different perspective, something other than the conventional way of doing things. But we need to know how to work with them rather than look for answers from them because they have flaws and biases. Understanding them and working together is more important that our design decisions being driven by them.

― Jiwon Jung

As a post-doctoral researcher in the surgery department of Erasmus MC, Jung is currently a part of the Consultation Room 2030 Convergence Flagship project. From this September, she will continue her work as an assistant professor at TU Delft’s Faculty of Industrial Design Engineering as well as working one day a week as an assistant professor in the surgery department of ErasmusMC. 

Design Methodologies

Jung’s research centred on developing a new methodology and tool to enhance data-enabled design for digital health. During her PhD, she worked closely with data scientists at TU Delft and guided master’s students to design a different way, using large-scale existing user experience data and machine learning, with the aim of making better digital health interventions.