Enabling artificial intelligence in high acuity medical environments

Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibility of device data due to the lack of adoption of open standards, and the complexity of regulatory/approval requirements for AI-based systems. The fragmentation of data, still being stored in isolated silos, results in limited accessibility for AI in healthcare and machine learning is complicated by the loss of semantics in data conversions. We outline a reference model that addresses the requirements of innovative AI-based research systems as well as the clinical reality. The integration of networked medical devices and Clinical Repositories based on open standards, such as IEEE 11073 SDC and HL7 FHIR, will foster novel assistance and decision support. The reference model will make point-of-care device data available for AI-based approaches. Semantic...