Every colon procedure (identified by ICD-9 code) performed between 10/1/ /31/2012 at 10 BJC Healthcare system adult hospitals were included. The electronic medical records were screened by a single clinical abstractor. Based on the abstracted information a single Infection Preventionist reviewed all procedures identified as potentially infected to determine infection status using 2012 National Healthcare Safety Network definitions. Specificity, sensitivity, positive and negative predictive values were calculated. CONCLUSION VALIDATION OF AN ELECTRONIC ALGORITHM TO IDENTIFY CANDIDATES FOR COLON SURGICAL SITE INFECTION REVIEW JA Yegge 1, K Gase 1, M Hohrein 1, H Xu 1, R Khoury 1, H Babcock 2 1 BJC HealthCare, St. Louis MO, 2 Washington University, St. Louis, MO Nothing to Disclose METHOD These results confirm that the algorithm is highly effective in rejecting true negatives for further evaluation. It is also highly effective in capturing true positives within the subset identified for infection investigation. Additional refinement of the algorithm rules is needed to decrease the number of procedures that are flagged for review. This will decrease the time the Infection Preventionist spends on chart review while not missing any infected cases Procedure s Identified 28% 115 Triggered Algorithm 72% 302 Did Not Trigger Algorithm 17 Confirmed Cases 1 Confirmed Case (Not Identified by Trigger) TestCalculation & Result Sensitivity17/(17+1) = 94.45% Specificity301/ (301+98) =75.44% Positive Predictive Value (PPV) 17/(17+98) = 14.79% Negative Predictive Value (NPV) 301/(301+1) = 99.67% RESULTS The objective of this study was to examine the algorithm’s accuracy as a surveillance method for colon SSIs. The algorithm rules look for readmissions after a qualifying procedure in addition to cultures, antibiotic starts and ICD-9 infection codes. The SSI candidates are sent to an Infection Preventionist’s work list for review. In response to an increasing surveillance burden, an electronic algorithm was developed in 2009 to identify surgical site infection (SSI) candidates.

Validation of an Electronic Algorithm to Identify Candidates for Colon Surgical Site Infection Review JA Yegge 1, K Gase 1, M Hohrein 1, H Xu 1, R Khoury 1, H Babcock 2 1 BJC HealthCare, St. Louis MO, 2 Washington University, St. Louis, MO METHODMETHOD RESULTSRESULTS CONCLUSIONCONCLUSION 417 Procedures Identified 28% 115 Triggered Algorithm 72% 302 Did Not Trigger Algorithm

417 procedures VALIDATION OF AN ELECTRONIC ALGORITHM TO IDENTIFY CANDIDATES FOR COLON SURGICAL SITE INFECTION REVIEW JA Yegge 1, K Gase 1, M Hohrein 1, H Xu 1, R Khoury 1, H Babcock 2 1 BJC HealthCare, St. Louis MO, 2 Washington University, St. Louis, MO METHOD CONCLUSION RESULTS In response to an increasing surveillance burden, an electronic algorithm was developed in 2009 to identify surgical site infection (SSI) candidates. The algorithm rules look for readmissions after a qualifying procedure in addition to cultures, antibiotic starts and ICD-9 infection codes. The SSI candidates are sent to an Infection Preventionist’s work list for review. 417 procedures 72% Did not trigger algorithm (302) 1 False negative 28% Triggered algorithm (115) 17 confirmed cases The objective of this study was to examine the algorithm’s accuracy as a surveillance method for colon SSIs. 3 months

Nothing to Disclose METHOD These results confirm that the algorithm is highly effective in rejecting true negatives for further evaluation. It is also highly effective in capturing true positives within the subset identified for infection investigation. Additional refinement of the algorithm rules is needed to decrease the number of procedures that are flagged for review. This will decrease the time the Infection Preventionist spends on chart review while not missing any infected cases. RESULTS In response to an increasing surveillance burden, an electronic algorithm was developed in 2009 to identify surgical site infection (SSI) candidates. The algorithm rules look for readmissions after a qualifying procedure in addition to cultures, antibiotic starts and ICD-9 infection codes. The SSI candidates are sent to an Infection Preventionist’s work list for review. Validation Of An Electronic Algorithm To Identify Candidates For Colon Surgical Site Infection Review JA Yegge 1, K Gase 1, M Hohrein 1, H Xu 1, R Khoury 1, H Babcock 2 1 BJC HealthCare, St. Louis MO, 2 Washington University, St. Louis, MO The objective of this study was to examine the algorithm’s accuracy as a surveillance method for colon SSIs. CONCLUSION 417 Procedures Identified 28% 115 Triggered Algorithm 72% 302 Did Not Trigger Algorithm 17 Confirmed Cases 1 Confirmed Case (Not Identified by Trigger) TestCalculation & Result Sensitivity17/(17+1) = 94.45% Specificity 301/ (301+98) =75.44% Positive Predictive Value (PPV) 17/(17+98) = 14.79% Negative Predictive Value (NPV) 301/(301+1) = 99.67% Every colon procedure (identified by ICD-9 code) performed between 10/1/ /31/2012 at 10 BJC HealthCare adult hospitals were included. The electronic medical records were screened by a single clinical abstractor. Based on the abstracted information a single Infection Preventionist reviewed all procedures identified as potentially infected to determine infection status using 2012 National Healthcare Safety Network definitions. Specificity, sensitivity, positive and negative predictive values were calculated.