ISDS Annual Conference Proceedings 2019. This is an Open Access article distributed under the terms of the Creative Commons 

AttributionNoncommercial 4.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 

distribution, and reproduction in any medium, provided the original work is properly cited. 

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 11(1): e278, 2019 

 

ISDS 2019 Conference Abstracts 

Electronic Processing of Antimicrobial Susceptibilities to 
Enhance Communicable Disease Surveillance 

Emily Roberts 

Informatics Program, Utah Department of Health, Salt Lake City, Utah, United States 

Objective 

Illustrate how the Utah Department of Health automatically processes antimicrobial susceptibility results that are received 
electronically 

Introduction 

The emerging threat of antimicrobial resistant organisms is a pressing public health concern. Surveillance for antimicrobial 
resistance can prevent infections, protect patients in the healthcare setting and improve antimicrobial use. In 2018, the Utah 
Department of Health mandated the reporting of antimicrobial susceptibility panels performed on selected organisms. Utah util izes 
the Electronic Message Staging Area (EMSA), a home-grown application to translate, process, and enter electronic laboratory 
results into UT-NEDSS, Utah’s integrated disease surveillance system. Processing these results electronically is challenging due 
to the need to interpret results based on the antimicrobial agent combined with the organism it was performed on. The receipt of 
antimicrobial susceptibility panels has required enhancements to EMSA for these results to be automatically processed. 

Methods 

Stand-alone antimicrobial susceptibility LOINCs are configured within EMSA to concatenate during the preprocessing stage. This 
tells EMSA that when this LOINC is sent within an HL7 message to find the organism name in the corresponding OBR 26.3 (the 
parent result field). EMSA then creates a new fabricated code that combines the antimicrobial agent with the organism identified 
from the culture (example: ‘18906-8 Pseudomonas aeruginosa’ is the fabricated code for Ciprofloxacin susceptibility to 
Pseudomonas aeruginosa). 

Once these new fabricated antimicrobial susceptibility codes are created, interpretation rules are programmed using current Clinical 
and Laboratory Standards Institute (CLSI) breakpoints for each unique organism/antimicrobial combination to determine if the 
result is Susceptible/Intermediate/Resistant. The interpreted test is then run through a set of condition-specific rules to determine 

how it should be included into UT-NEDSS. 

Results 

Antimicrobial susceptibility panels performed on Acinetobacter species, Escherichia coli, Klebsiella species, Pseudomon as 
aeruginosa, Enterobacter species, Candida auris/haemulonii, Mycobacterium tuberculosis, Neisseria gonorrhoeae, Salmonella 
species, Shigella species, Streptococcus pneumoniae and invasive Staphylococcus aureus are now included in Utah’s 
Communicable Disease Reporting Rule. Currently, there are 36 antimicrobial agents programmed into EMSA and there are a total 
of 217 antimicrobial susceptibility codes programmed into the system. 

Conclusions 

Processing electronic antimicrobial susceptibility results presents unique challenges for processing. Interpretation of results can 
vary based on test method, performing laboratory, and organism. Enhancing functionality within EMSA was necessary for 
combining the antimicrobial agent and organism it was performed on. Implementing systems capable of automatically processing 
complicated antimicrobial susceptibility results should be a priority for any health department interested in expanding their  
communicable disease rule to include antimicrobial susceptibility testing. 

 

http://ojphi.org/