Critically ill patients often experience pain during their treatment but due to patients’ lowered ability to communicate, pain assessment may be challenging. The aim of the study was to develop the concept of the Smart Pain Assessment tool based on the Internet of Things technology for critically ill patients who are unable to communicate their pain.

The study describes two phases of the early stage development of the Smart Pain Assessment tool in a medical device development framework. The initiation Phase I consists of a scoping review, conducted to explore the potentiality of the Internet of Things technology in basic nursing care. In the formulation Phase II, the prototype of the Smart Pain Assessment tool was tested and the concept was evaluated for feasibility. The prototype was tested with healthy participants (n=31) during experimental pain, measuring pain-related physiological variables and activity of five facial muscles. The variables were combined using machine learning to create a model for pain prediction. The feasibility of the concept was evaluated in focus group interviews with critical care nurses (n=20) as potential users of the device.

The literature review suggests that the development of Internet of Things -based innovations in basic nursing care is diverse but still in its early stages. The prototype was able to identify experimental pain and classify its intensity as mild or moderate/severe with 83% accuracy. In addition, three of the five facial muscles tested were recognised to provide the most pain-related information. According to critical care nurses, the Smart Pain Assessment tool could be used to ensure pain assessment, but it needs to be integrated into an existing patient monitoring and information system, and the reliability of the data provided by the device needs to be assessable for nurses.

Based on the results of this study, detecting and classifying experimental pain's intensity automatically using an Internet of Things -based device is possible. The prototype of the device should be further developed and tested in clinical trials, involving the users at each stage of the development to ensure clinical relevance and a user-centric design.