A comparison of methods to detect urinary tract infections using electronic data

• Published in: Joint Commission journal on quality and patient safety Vol. 36; no. 9; p. 411
• Main Authors: Landers, Timothy, Apte, Mandar, Hyman, Sandra, Furuya, Yoko, Glied, Sherry, Larson, Elaine
• Format: Journal Article
• Language: English
• Published: Netherlands 01.09.2010
• Subjects: Decision Support Techniques; Electronic Data Processing; Electronic Health Records; Hospital Information Systems; Humans; International Classification of Diseases; Medical Audit; Population Surveillance - methods; Urinary Tract Infections - classification; Urinary Tract Infections - diagnosis; Urine - microbiology
• ISSN: 1553-7250
• DOI: 10.1016/S1553-7250(10)36060-0

The use of electronic medical records to identify common health care-associated infections (HAIs), including pneumonia, surgical site infections, bloodstream infections, and urinary tract infections (UTIs), has been proposed to help perform HAI surveillance and guide infection prevention efforts. Increased attention on HAIs 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. Seven computer-based decision rules to identify UTIs were compared in a sample of 33,834 admissions to an urban academic health center. These decision rules included combinations of laboratory data, patient clinical data, and administrative data (for example, International Statistical Classification of Diseases and Related Health Problems, Ninth Revision [ICD-9] codes). Of 33,834 hospital admissions, 3,870 UTIs were identified by at least one of the decision rules. The use of ICD-9 codes alone identified 2,614 UTIs. Laboratory-based definitions identified 2,773 infections, but when the presence of fever was included, only 1,125 UTIs were identified. The estimated sensitivity of ICD-9 codes was 55.6% (95% confidence interval [CI], 52.5%-58.5%) when compared with a culture- and symptom-based definition. Of the UTIs identified by ICD-9 codes, 167/1,125 (14.8%) also met two urine-culture decision rules. Use of the example of UTI identification shows how different algorithms may be appropriate, depending on the goal of case identification. Electronic surveillance methods may be beneficial for mandatory reporting, process improvement, and economic analysis.