An exploration of theories, models and concepts to formulate HCI guidelines for the realization of medical, shared decision making systems.

Abstract

Cardiovascular diseases stand out as the leading cause of death globally. Following a cardiovascular incident, patients are encouraged to engage in secondary prevention, including cardiac rehabilitation, to facilitate recovery and decrease the likelihood of recurrent events. This typically involves enrollment in a cardiac rehabilitation (CR) program comprising various essential components such as parameter monitoring, medication, physical activity, healthy nutrition, smoking cessation, stress management, and education. In recent years, there has been a notable surge in patients’ interest in taking control of their own health and rehabilitation journey. Shared decision making (SDM) emphasizes the involvement of patients in making decisions tailored to their health and care preferences. Effective support for both patients and caregivers engaged in the SDM process relies on the utilization of comprehensive tools that elucidate available evidence and encourage patient preference elicitation. Furthermore, these tools serve to facilitate the SDM process and provide assistance throughout subsequent follow-up phases. This PhD project investigates the potential of digital tools in facilitating shared decision making within the different phases of secondary prevention of cardiovascular diseases. The first part of this dissertation introduces conceptual frameworks and guiding principles for the design of digital tools aimed at supporting SDM processes for behaviour change. Part two demonstrates the practical application of the proposed frameworks and principles by designing SDM tools for cardiovascular disease management. Finally, Part III delves into the aspect of personalization and its impact on health behaviour change. The user studies conducted in this doctoral research indicated that cardiac patients and their caregivers are willing to perform shared decision making using the designed approaches and tools. Recently completed and ongoing randomized controlled clinical trials will soon provide more insight into the medical effectiveness of the proposed SDM approaches and accompanying tools. While the concepts and applications presented in this PhD project show promising outcomes, they also reveal certain limitations that indicate directions for future research. These include exploring the capabilities of artificial intelligence, integrating informal caregivers and the patient’s social network into the decision-making processes, and exploring SDM for other chronic diseases.