A Comparison of Automated Methicillin-Resistant Staphylococcus aureus Identification with Current Infection Control Practice

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.

[1]  Clement J. McDonald,et al.  An effective computerized reminder for contact isolation of patients colonized or infected with resistant organisms , 2008, Int. J. Medical Informatics.

[2]  C. McDonald Protocol-based computer reminders, the quality of care and the non-perfectability of man. , 1976, The New England journal of medicine.

[3]  R. Scott Evans,et al.  Application of Information Technology: Rapid Identification of Hospitalized Patients at High Risk for MRSA Carriage , 2008, J. Am. Medical Informatics Assoc..

[4]  David W Bates,et al.  Safety of patients isolated for infection control. , 2003, JAMA.

[5]  D. Sahm,et al.  Laboratory-based surveillance of current antimicrobial resistance patterns and trends among Staphylococcus aureus: 2005 status in the United States , 2006, Annals of Clinical Microbiology and Antimicrobials.

[6]  R M Gardner,et al.  Computer surveillance of hospital-acquired infections and antibiotic use. , 1986, JAMA.

[7]  S. Cosgrove,et al.  The Impact of Methicillin Resistance in Staphylococcus aureus Bacteremia on Patient Outcomes: Mortality, Length of Stay, and Hospital Charges , 2005, Infection Control & Hospital Epidemiology.

[8]  Lonnie Blevins,et al.  The Indiana network for patient care: a working local health information infrastructure. An example of a working infrastructure collaboration that links data from five health systems and hundreds of millions of entries. , 2005, Health affairs.

[9]  Clement J. McDonald,et al.  Research Paper: Use of a Regional Health Information Exchange to Detect Crossover of Patients with MRSA between Urban Hospitals , 2008, J. Am. Medical Informatics Assoc..

[10]  Roberta B Carey,et al.  Invasive methicillin-resistant Staphylococcus aureus infections in the United States. , 2007, JAMA.

[11]  J. Marc Overhage,et al.  Using Natural Language Processing to Improve Accuracy of Automated Notifiable Disease Reporting , 2008, AMIA.