The prevalence of healthcare-associated infections (HAI) stresses the need for automatic surveillance in order to follow the effect of preventive measures.
A number of detection systems have been set up for several languages, but none is known for Swedish hospitals. We plan a series of infection type specific programs for detection of HAI in electronic health records at a Swedish university hospital. Also, we aim at detecting HAI for patients entering hospital with HAI from previous care, a task that is not often addressed. This first study aims at surveillance of healthcare-associated urinary tract infections. The created rule-based system depends on acquiring the essential clinical information, and a combination of data and text mining is used. The wide range of diverse clinics with different traditions of documentation poses difficulties for detection. Results from evaluation on 1,867 care episodes from Oncology and Surgery show high precision (0.98), specificity (0.99) and negative predictive value (0.99), but an intermediate recall (0.60). An error analysis of the evaluation is presented and discussed.
Written by:
Tanushi H, Kvist M, Sparrelid E. Are you the author?
Dept. of Quality and Patient Safety, Karolinska University Hospital, Sweden; Dept. of Computer and Systems Sciences (DSV), Stockholm University, Sweden.
Reference: Stud Health Technol Inform. 2014;207:330-9.
doi: 10.3233/978-1-61499-474-9-330
PubMed Abstract
PMID: 25488239