A Comparison of Methods to Detect Urinary Tract Infections Using Electronic Data

Abstract

Background-The use of electronic medical records to identify common healthcare-associated infections (HAIs), including pneumonia, surgical site infections, bloodstream infections and urinary tract infections (UTIs), has been proposed as an efficient tool to perform HAI surveillance and to guide infection prevention efforts. Increased attention on HAIs as a safety and quality issue has led to public health reporting requirements and a focus on quality improvement activities around HAIs. Traditional surveillance to detect HAIs and focus prevention efforts is labor intensive and computer algorithms could be useful to screen electronic data and provide actionable information.

Point prevalence and risk factors for healthcare-associated infections in primary healthcare wards

“…However, the occurrence of UTIs with the McGeer definitions may be overestimated, as, for example, a change in the patient's urine outlook is one criterion for infection in the McGeer definitions. In one recent study, it was shown that positive urinary culture, pyuria, and fever as a prerequisite for infection markedly decreased the number of UTI diagnoses [35].…”

Trends in mortality, length of stay, and hospital charges associated with health care–associated infections, 2006-2012

“…[16–18] The algorithms used a combination of microbiologic culture results, urine microscopy, and ICD-9-CM procedure and diagnosis codes to identify four types of infection: BSI, UTI, SSI, and pneumonia. For each of these four infection types, patient discharges were classified as either ‘infected’ or ‘uninfected.’ In order to reduce misclassification, patient discharges were considered ‘indeterminate’ if infection status could not be definitively assessed based on the available electronic data.…”

Costs of Healthcare- and Community-Associated Infections With Antimicrobial-Resistant Versus Antimicrobial-Susceptible Organisms

“…Recognizing the limitations of infection definitions utilizing electronic data only, we categorized patients into 3 groups: infected, noninfected, and uncertain (Supplementary Data), which allowed us to exclude patients with uncertain infection status to limit bias due to misclassification. We used a combination of microbiologic results, clinical symptoms, urine microscopy results, and International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD-9-CM ) diagnosis codes to identify patients with an infection with the causative organism [8, 9]. …”