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.