The SARS-CoV-2 pandemic has brought unexpected new scenarios in patient-care journeys and has accelerated this innovative process in the healthcare sector, demonstrating the importance of a systemic rethinking of remote care, mostly when patients are discharged from the hospital and continue their therapies at home in autonomy. The possibility to remotely monitor patients at home by means of smart sensors and medical devices has a dramatic impact on the quality of health services. Situation awareness plays an essential role in the decision-making process about the users, patients in this case, and their behaviors. Leveraging an Edge Computing framework, with embedded Artificial Intelligence capabilities to process near real-time data gathered from connected smart devices, would provide automatic decision support, thus improving the physicians’ course of action. In this paper we introduce, within an Edge AI framework, a dedicated module, called Clinical Pathway Adherence Checker (CPAC), which identifies the discrepancies between the modeled clinical pathway and the observed one by means of process mining techniques, and hence detecting early clinical deterioration of patient conditions. Also, further analyses are conducted in the anomaly detection at the Edge that may occur during the health data transmission process. © 2021, Springer Nature Switzerland AG.
Management at the Edge of Situation Awareness During Patient Telemonitoring