title: A comparison of automated methicillin-resistant Staphylococcus aureus identification with current infection control practice.
abstract: Infections with Methicillin-Resistant Staphylococcus aureus (MRSA) account for almost 20,000 deaths per year. Early identification of patients with MRSA infection or colonization aids in stopping spread. We compared automated identification of MRSA using HL7 lab result messages to current manual infection control practices at a local hospital during July-September 2008. We used data from infection control providers (ICPs), the microbiology lab, and a Regional Healthcare Information Exchange to assess the accuracy of manual and automated methods. Three hundred seventy MRSA cases were identified from July-September 2008. Manual identification recognized 314 (sensitivity 84.9%, positive predictive value 99.4%) MRSA cases and automated detection from HL7 messages identified 341 (sensitivity 92.2%, positive predictive value 98.8%). Automated processing of HL7 lab report messages is a more sensitive method of capturing MRSA cases than current standard infection control practice, with minimal loss of specificity.
authors.affiliation: Regenstrief Institute, Inc., and Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: David
authors.initials: D
authors.lastname: Shepherd
authors.forename: Jeff
authors.initials: J
authors.lastname: Friedlin
authors.forename: Shaun
authors.initials: S
authors.lastname: Grannis
authors.forename: Siu
authors.initials: S
authors.lastname: Hui
authors.forename: Abel
authors.initials: A
authors.lastname: Kho
mesh_terms: D002984:Clinical Laboratory Information Systems; D001330:Electronic Data Processing; D006761:Hospitals; D006801:Humans; D017053:Infection Control / Q000379:methods*; D008485:Medical Audit; D055624:Methicillin-Resistant Staphylococcus aureus / Q000302:isolation & purification*; D013203:Staphylococcal Infections / Q000175:diagnosis* / Q000382:microbiology / Q000517:prevention & control