David B. Ross


June 2012

Document Type


Degree Name



Dept. of Medical Informatics and Clinical Epidemiology


Oregon Health & Science University


Antimicrobial resistance (AR) is a serious clinical and public health problem, driven largely by inappropriate use of antimicrobials, particularly in the setting of empiric therapy of infections. Provision of local AR prevalence data to clinicians in the form of a cumulative antibiogram (CABGM) can provide decision support for antimicrobial prescribing, decreasing inappropriate use, improving treatment outcomes, and minimizing selection of AR organisms. However, CABGMs are produced by widely varying methods from different data sources with different vocabularies, resulting in inaccurate data presentations, lack of compliance with published standards, and inability to compare AR rates between different health care facilities for quality improvement. To address this, I describe a data model constraining a CABGM to a published standard using appropriate vocabularies. This standard data model can be used to construct CABGMs for major use cases, including use as a decision support tool for clinicians choosing empiric antimicrobial therapy, as a surveillance tool for hospital epidemiologists and public health officials comparing AR prevalence rates between hospitals, and as a quality improvement tool for aggregating AR prevalence data from different institutions. This data model may be used to construct a relational database model; however, clinical microbiology and patient data are frequently stored in electronic medical records using a hierarchical database model. Using the electronic medical record and clinical data warehouse employed by the Veterans Health Administration as an example, I outline an implementation scheme for migrating clinical and microbiological data from hierarchical databases to a relational database defined by the model. Finally, vocabularies used to describe microbiologic concepts and store clinical microbiology data frequently differ between different health care facilities. I discuss approaches to mapping semantic equivalents used for microbiology data to a common set of standard terms.




School of Medicine



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