Medical Data and Services in Efficient Personal Care Delivery: A Novel Dynamic Semantic Microservice CQRS Framework

Healthcrises often overwhelm people, causing significant stress for physics not only during the active operationalperiod but also throughout the entire patient care life cycle.In these kinds of scenarios, the pressure on medical professionalscan lead to service inconsistencies and increased complexity in delivering ongoing and coordinated healthcare services.Previous research studies have been mostly focused on wireless biomedical sensors and big data analytics, often neglectingpatient context under several moving locations where service tends to break as a result of unexpected events while running.To address this gap, we explore Command Query Responsibility Segregation (CQRS) pattern and ConteXt-aware AgentOriented Semantic MicroServices (CxASMS), to ensure service continuity with less computational complexity. As the casestudy, we employed pregnant women, which is often used in the health field. Results of our study prove that context-awaresemantic models based on CQRS can deal with service volatility induced by context changes while improving accuracythroughout the semantic service life cycle. The proposed platform reuses previous responses and views within the patientand service contexts to optimize the response time. This is the first dynamic semantic CQRS framework in the field of smarthealth for service continuity using context-aware sensing, which is then properly evaluated using a real-world study