Comparison of Two Computer Algorithms To Identify Surgical Site Infections

Abstract Background: Surgical site infections (SSIs), the second most common healthcare-associated infections, increase hospital stay and healthcare costs significantly. Traditional surveillance of SSIs is labor-intensive. Mandatory reporting and new non-payment policies for some SSIs increase the need for efficient and standardized surveillance methods. Computer algorithms using administrative, clinical, and laboratory data collected routinely have shown promise for complementing traditional surveillance. Methods: Two computer algorithms were created to identify SSIs in inpatient admissions to an urban, academic tertiary-care hospital in 2007 using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (Rule A) and laboratory culture data (Rule B). We calculated the number of SSIs identified by each rule and both rules combined and the percent agreement between the rules. In a subset analysis, the results of the rules were compared with those of tra...