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J Med Syst. 2016 Jan;40(1):23. doi: 10.1007/s10916-015-0364-6. Epub 2015 Nov 4.

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

Author information

1
School of Public Health, University of California at Berkeley, 50 University Hall, 94720-7360, Berkeley, CA, USA. arash.shaban-nejad@berkeley.edu.
2
Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada. arash.shaban-nejad@berkeley.edu.
3
Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada.
4
IPSNP Computing Inc, Suite 1000, 44 Chipman Hill, Station A, PO Box 7289, Saint John, NB, E2L 4S6, Canada.
5
Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
6
Department of Computer Science, University of New Brunswick, Saint John, NB, Canada.

Abstract

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)).

KEYWORDS:

Healthcare-associated infections; Knowledge modeling; Ontologies; Semantic framework; Surgical site infections; Surveillance

PMID:
26537131
DOI:
10.1007/s10916-015-0364-6
[Indexed for MEDLINE]

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