Clinician Utilization of Rapid Antibiotic Susceptibility Data: A Prospective Study

  • Franklin P. Koontz
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 349)


The University of Iowa Hospitals is a 1,000 bed tertiary care center which serves as a regional hospital for hematology-oncology, cystic fibrosis and a variety of chronic disease patients. It also houses diverse transplantation patients, ranging from kidney, heart, liver-pancreas, to bone marrow. Thus a fair percentage of our patient load is compromised by immunosuppression or chronic disease which influences the demand for rapid testing and equally rapid reporting systems. In the areas of bacterial identification and susceptibility tests (MICs), we utilize the Vitek System as the mainstay of the laboratory due to its rapid turnaround time and reproducible accuracy. If we encounter isolates not in the Vitek database we use the manual API for idents and B-D Sceptor for MICs. I’ll try to detail this as we follow a specimen through our work-flow pattern. To fully appreciate the Iowa System of rapid testing and reporting, one must understand that we are one of the most automated and instrumented laboratories in the country. All steps of specimen handling from admit or rejection, to final report, are computer mediated which gives us reproducibility as well as rapid, accurate test results. When the specimen is received in the lab and entered into our system via the lab computer, a work-flow card listing the media and stain, etc., required for that specific type of specimen is printed out. This will vary with specimen types, e.g., a wound would get routine culture and Gram stain only, however an abscess would also get anaerobic culture, while a BAL would demand all of these plus culture and stains for fungi or TB, if from the appropriate patient load.


Coagulase Negative Staph Patient Load Chronic Disease Patient Iowa Hospital Nosocomial Sepsis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Franklin P. Koontz
    • 1
  1. 1.University of Iowa HospitalsIowa CityUSA

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