AI systems in clinical practice

Laboratory systems
SahmAlert
System to assist Microbiology laboratory with identifying organisms that have unusual patterns of antibiotic resistance

developed by clinical domains keywords
Washington University, St. Louis Microbiology laboratory, antibiotic resistance Expert systems, rules-based systems.
location commissioned status
Barnes Hospital, St. Louis, Missouri October 1995
description
To test the efficacy of antibiotics, microbiologists apply clinically approved drugs to bacterial cultures. Drugs which are effective against the microorganisms in the culture may then be considered therapeutically useful in treating a patient with the same type of microbial infection. However, bacteria are developing resistance to existing antibiotics, making previously routine infections difficult or even impossible to treat. This makes the task of developing new antibiotics difficult. It also complicates the work of the health care provider, who must stay abreast of these changes. Microbiology culture data from the hospital's laboratory system are monitored by SahmAlert. Using a rulebase consisting of criteria developed by local epidemiologists, SahmAlert scans the culture data, identifying which cultures contain organisms with patterns of unusual antibiotic resistance.

Languages/Shells Used: CLIPS, Sybase ISQL scripts, Bourne shell scripts.

references

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contact links

Washington University School of Medicine
Department of Internal Medicine
Division of Medical Informatics
660 South Euclid Campus
Box 8005 St. Louis
Missouri 63110 USA.

acknowledgements

Archive of AI systems in clinical practice previously administered by Enrico Coiera. Used with permission. Maintained and extended since 2001 by OpenClinical.

Entry on archive: October 27 1995
Last main update: October 27 1995
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