Early Implementation of a Regional Electronic Infection Control Network using Parsed Microbiology Culture Data
Methods: We built 1) an HL7v2 correction engine that deals with incorrect microbiology message structure and content, 2) decision support to identify superbugs of interest, 3) an admission/discharge/transfer message processor with cross-institutional identity matching to generate alerts upon subsequent ED or inpatient admission, and 4) secure email alerts to the IP(s) at the admitting institution. The HL7v2 correction engine parses key data elements needed for alerts: organism, antibiotics tested, minimum inhibitory concentrations, susceptibility interpretation, body source of the culture, and health care facility where drawn. The five GNRMDRO categories are 1) Enterobacteriaceae with extended-spectrum beta lactamase (ESBL-E), 2) carbapenem resistant Enterobacteriaceae (CRE), 3) Pseudomonas aeruginosa resistant to 3 of 4 antibiotic classes, 4) Acinetobacter baumannii resistant to 3 of 4 antibiotic classes, and 5) other.
Results: In the first 12 weeks, email alerts to the IPs were generated for 105 distinct patients (69 with ED visit only, 36 with hospital admission). Five hospital systems were alerted, for 21 distinct hospitals. The GNRMDROs were ESBL-E (84%), CRE (N=7 [7%]: 6 Klebsiella pneumoniae, 1 Serratia marcescens), Pseudomonas (5%), Acinetobacter (4%), and other (1%). Body sources were urine (76%), blood (8%), and other (16%) including wound, bronchoalveolar lavage, sputum, or bile. For 26 (25%) of 105 patients, the admitting hospital system was different from the one where the GNRMDRO culture had been drawn.
Conclusion: The amount of cross-over between hospitals – by patients colonized or infected with gram-negative superbugs – is striking. Timely alerts may hasten placement into contact isolation and thereby may help reduce the spread of life-threatening bacteria.
S. M. E. Finnell, None
S. Khokhar, None
J. Egg, None
L. Lemmon, None
D. Shepherd, None
J. Friedlin, None
X. Li, None
A. Kho, None
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