Towards Complex Event Processing for Clinical Decision Support using FHIR

Clinical Decision Support (CDS) is designed to provide medical guidance and decision support based on patient information. More and more patient data is being collected from medical devices and fitness trackers, and some decisions need to be made quickly to allow for timely medical treatment. In our
work, we combine a Semantic Web rule-based approach for representing medical guidelines with medical data represented in the "Fast Healthcare Interoperability Resources" (FHIR) standard. We implement a complex event processing strategy to deliver near-real-time decisions for a physician. This is relevant in intensive care and emergency medicine in particular, as medical treatment is time-critical and can decide on life or death of the patients. Different approaches exist for real-time clinical decision support. However, the integration of FHIR and Semantic Web technologies is missing. By integrating FHIR,
medical ontologies, and Prova as a semantic rule engine, we can define the medical guideline and include hooks for data extraction and decision points. We describe the entire process, starting with the medical device submitting the FHIR observations, through the data handling and decision points, to the
notification of the attending physician. We extend the functionality of the existing rule engine for data handling and decision-making. In this work, we outline a solution that is capable of clinical decision support, including FHIR, and makes use of a semantic rule engine that allows medical guidelines to
be expressed in RuleML. With these features, we also include the option of real-time complex even processing