Navigating consequential decisions is a difficult task in and of itself, especially when they have a significant impact on one's life. This is especially true in the complex world of healthcare, where the importance of choices is magnified. The complexities of these issues can make it difficult for patients and their loved ones to effectively address them while dealing with increased stress and uncertainty. Medical professionals are also under immense pressure to ensure the well-being of their patients.

In such scenarios, the indispensable role of decision support tools (DSTs) becomes apparent. These invaluable resources aid both patients and healthcare professionals in selecting the optimal treatment option by carefully considering the risks and benefits involved. DSTs play a vital role in empowering individuals to make well-informed decisions by providing relevant information and facilitating comprehensive analysis. These tools enable the evaluation of various treatment options or potential outcomes. Some DSTs are data-driven, relying on prognostic algorithms. By utilizing analytical methods and algorithms on clinical data, they can offer predictions on survival rates, chances of recurrence, and estimated quality of life, particularly in diseases such as cancer.

Although data scientists have worked consistently to develop algorithms and guarantee the validity of the data used, there has been a noticeable lack of focus on defining the qualities of appropriate interaction with decision support tools. Numerous critical aspects remain unclear, such as identifying the appropriate qualities of interaction with a DST, determining the optimal delivery method for these tools, determining the optimal point in the care path to introduce them, specifying the relevant data to be provided to the DST, and deciding what information should be delivered to empower patients in their decision-making process. Furthermore, the integration and practical implementation of DSTs within the time limitations and complex dynamics of the medical context have been widely disregarded.

In this graduation project, we adopt a speculative design perspective to explore the future of data-driven healthcare. We aim to imagine how DSTs can become meaningful and sustainable components of the care path. Through a process of futurology, we envision an alternative future (or present) to contribute to the doctor and patient (as human actors) seeing the DST (the non-human actor) literally as partners in making decisions.

Problem Background:
Cardiovascular disease (CVD) is the most common cause of death in the world (Johnston et al., 2016). One of the main causes for cardiovascular mortality is a myocardial infarction (MI) (Jneid et al., 2013; Mishra & Ramavataram, 2021; Verburg et al., 2019). A myocardial infarction is an event in which the heart muscle is damaged because of a complete or partial absence of oxygen supply (Mishra & Ramavataram, 2021)

The healthcare system is pressured by the enormous number of CVD cases (Kaushik et al., 2020). Digital care has gained recognition in the past decade because of its potential to release some pressure from the healthcare system. With the potential of digital care in mind, Leiden University Medical Center (LUMC) has developed a concept called “The MI Box” which provides patients who have had an MI with self-monitoring devices (The Box, 2021).
During the hospitalisation phase of the MI care trajectory, there is little time to involve patients in their care because healthcare professionals (HCPs) need to work as quickly as possible to avoid as much heart damage as possible. This is causing patients to have a bad discharge experience. The MI Box shows potential to involve patients more after the discharge as the concept shows the monitored values to patients. However, it can be difficult for patients to understand these values and how to use them for self-care activities, especially because of the little involvement during hospitalisation. If LUMC wants to satisfy its aim to change from monitoring to coaching, it is crucial to provide patients with enough support to work with their monitored values and perform self-care activities.

Increasing patient involvement is an essential first step before shared decision making (SDM) can be realised. SDM is a suitable approach for the change towards coaching as it invites patients to make their own care choices. In the MI care trajectory, the implementation of SDM is currently lacking. Therefore, further analysis of SDM is needed. A tool that is valuable for the analysis and increasement of SDM, and which is used in this thesis, is the Metro Mapping tool (Metro Mapping, n.d.).

Besides, because the MI Box causes care to be transferred to a patient’s home environment, it is necessary to consider the home context of a patient as care activities do not happen in a controlled hospital environment. Currently, little is known about the context in which the MI Box is used. Therefore, more knowledge about the context is necessary to consider what support means would be suitable to offer to patients to make them more empowered to perform self-care activities.

Research Question:
How to shift from monitoring to coaching with the MI Box?

Research activities:
-Literature research;
-Interviews: HCPs (n=5), MI patients (n=4), MI Box users (n=2);
-Context mapping;
-Metro Mapping.

Design Goal:
The goal is to increase patients’ confidence to perform self-care activities by increasing patients’ involvement and guidance with the use of the MI Box.

Final Concept:
“UnBoxing the MI Box”

You, I and almost everyone deal with illness under a certain condition. As digital health is becoming ever more widespread nowadays, this change shapes a new understanding of our medical experience. Consider how these new digital tools might affect patient experience becomes more crucial for everyone’s life. Four studies, aiming at understanding and enhancing the patient experience, thus become the foundation of this project.

However, the transition of academic research knowledge into practical design information often faces obstacles (Zielhuis et al., 2022a). Challenges include effectively teach-ing freshmen (Hoadley & Cox, 2008), selecting the right formats, and successfully conveying academic knowledge in a practical context (Stappers and Giaccardi, 2017). As a result, despite the fact that the primary research potentially enables healthcare designers to enhance the digital patient experience, whether practitioners can benefit from these academic findings is still unclear. Therefore, to further transfer the primary research to support design education and best practices for improving patient experience in digital health, as well as increase the accessibility and applicability of the primary research (Daniluk and Koert, 2015; Cook, D. A., 2007), an exploration starts.

Desk research and literature research are done to define the project’s scope and make a concrete goal. Design guidelines are considered since they play a crucial role in leading designers to success. Through learning and following effective design guidelines, designers can significantly enhance the quality of their design outcomes (Fu, Yang, & Wood, 2016). To transform the primary research into design guidelines, nine qualities should be considered in the creation and evaluation process. Additionally, the website is an appro-priate method of transferring information (Daniluk and Koert, 2015; Cook, D. A., 2007). When creating a website, usability, accessibility, and consistency need to be considered.

It is defined that the primary research should be transformed into design guidelines based on a website platform, the design goal therefore is defined as: To transform the primary research into a good and usable web-based design guide, enabling healthcare designers to utilize the primary research to improve the digital patient experience. To achieve this goal, design activities should be conducted to ensure the website’s usability, consistency, and accessibility, with the guide content’s clarity, efficacy, and credibility.
Through design activities such as walkthroughs and case studies, an initial minimum viable (MVP) website is developed. An evaluation workshop makes clear that the website partially meets
its design objectives and suggests a need for improvement in content clarity and efficacy and website usability. Following this feedback, an iteration is developed, resulting in a complete website design. A small-scale usability test validates the in-crease in content clarity, credibility, and efficacy, with overall usability slightly declining. Hence, the website is iterated again.
In conclusion, there was proof that the final design met the goal of having expected clarity, efficacy, credibility, color accessibility, and internal and external consistency. The final SUS score was 59.5 out of 100, which was acceptable but implied room for improvement (Bangor, Kortum, & Miller, 2009).