A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores

A bedsore, also known as pressure sore, pressure ulcer or decubitus ulcer, is the result of constant pressure on skin occurring in bedridden patients and paraplegics continuously sitting in chair. All patients who are immobile for a long time due to any cause are likely to get bedsores. Effective and efficient management of processes related to the treatment of bedsores is an important issue for healthcare organizations as it heavily affects the quality of life of patients and the costs for such organizations. Therefore organizations need and look for more and more to provide their field workforce with smart mobile tools able to support such processes. In such a context, this paper proposes a mobile app implementing a Clinical Decision Support System (CDSS) to help field operators to measure the bedsore, classify its status, trace its evolution along the timeline and making correct decisions about the course of actions to effectively treat it. The mobile app is mostly based on Augmented Reality supported by Deep Learning, thus it requires an adequate system architecture to be effectively deployed, adopted and used. From the conceptual viewpoint, the defined CDSS model lays on three important considerations: providing automatic support to classify the status of a bedsore does not do all the work but help operators to improve the quality of their decisions, augmented reality allows to build a situated environment for decision-making supporting the operators’ cognitive processes, operators should use only one tool to execute all their tasks in order to be more focused on the real problem which is to improve the quality of life of their patients.