This PhD research evaluates the biocompatibility and functionality of the developed sensors, both in vitro and in vivo. The results will be shared with the engineers to further improve the sensor design and measurement performance. The measured parameters will also be analyzed to better understand the underlying causes of the observed changes. The clinical applicability of the sensors will be evaluated using human ex vivo models. 

Furthermore, it will be assessed whether the combined sensor system can reliably measure wound-related parameters and monitor changes during the wound-healing process in a diabetic setting. 

Wound care depends on clinical reasoning and decision-making. At every dressing change, nurses read the wound, weigh what they see against the patient in front of them, and decide what comes next. This PhD examines how nurses reason and make decisions in complex wound management, and how smart dressings and AI-based decision support can strengthen that process without replacing it.

First, a constructivist grounded theory study develops a theory of how nurses reason and make decisions in wound care practice. Second, a think-aloud study captures this reasoning in real time during simulated scenarios. Clinicians, engineers and data scientists then come together in design-thinking workshops to imagine a feasible future for data-informed wound care. The nurses' decision-making is also translated into standardised nursing language that AI systems can use.

Finally, the technologies are tested in simulated clinical practice to see whether they are feasible, acceptable and appropriate. The result is a set of tools that support nurses' clinical decisions: informed by data, but still guided by clinical expertise.