From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections

We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections - Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers. The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients undergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)).

HAIKU: A Semantic Framework for Surveillance of Healthcare-Associated Infections

Healthcare-Associated Infections (HAI) impose a substantial health and financial burden. Surveillance for HAI is essential to develop and evaluate prevention and control efforts. The traditional approaches to HAI surveillance are often limited in scope and efficiency by the need to manually obtain and integrate data from disparate paper charts and information systems. The considerable effort required for discovery and integration of relevant data from multiple sources limits the current effectiveness of HAI surveillance. Knowledge-based systems can address this problem of contextualizing data to support integration and reasoning. In order to facilitate knowledge-based decision making in this area, availability of a reference vocabulary is crucial. The existing terminologies in this domain still suffer from inconsistencies and confusion in different medical/clinical practices, and there is a need for their further improvement and clarification. To develop a common understanding of the infection control domain and to achieve data interoperability in the area of hospital-acquired infections, we present the HAI Ontology (HAIO) to improve knowledge processing in pervasive healthcare environments, as part of the HAIKU (Hospital Acquired Infections - Knowledge in Use) system. The HAIKU framework assists physicians and infection control practitioners by providing recommendations regarding case detection, risk stratification and identification of diagnostic factors.