title: Electronic measures of surgical site infection: implications for estimating risks and costs.
abstract: OBJECTIVE
  Electronic measures of surgical site infections (SSIs) are being used more frequently in place of labor-intensive measures. This study compares performance characteristics of 2 electronic measures of SSIs with a clinical measure and studies the implications of using electronic measures to estimate risk factors and costs of SSIs among surgery patients.
  
  
  METHODS
  Data included 1,066 general and vascular surgery patients at a single academic center between 2007 and 2008. Clinical data were from the National Surgical Quality Improvement Program (NSQIP) database, which includes a nurse-derived measure of SSI. We compared the NSQIP SSI measure with 2 electronic measures of SSI: MedMined Nosocomial Infection Marker (NIM) and International Classification of Diseases, Ninth Revision (ICD-9) coding for SSIs. We compared infection rates for each measure, estimated sensitivity and specificity of electronic measures, compared effects of SSI measures on risk factors for mortality using logistic regression, and compared estimated costs of SSIs for measures using linear regression.
  
  
  RESULTS
  SSIs were observed in 8.8% of patients according to the NSQIP definition, 2.6% of patients according to the NIM definition, and 5.8% according to the ICD-9 definition. Logistic regression for each SSI measure revealed large differences in estimated risk factors. NIM and ICD-9 measures overestimated the cost of SSIs by 134% and 33%, respectively.
  
  
  CONCLUSIONS
  Caution should be taken when relying on electronic measures for SSI surveillance and when estimating risk and costs attributable to SSIs. Electronic measures are convenient, but in this data set they did not correlate well with a clinical measure of infection.
authors.affiliation: Division of Outcomes Research and Quality, Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania 17033, USA. chollenbeak@psu.edu
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Hollenbeak
authors.forename: Melissa M
authors.initials: MM
authors.lastname: Boltz
authors.forename: Lucas E
authors.initials: LE
authors.lastname: Nikkel
authors.forename: Eric
authors.initials: E
authors.lastname: Schaefer
authors.forename: Gail
authors.initials: G
authors.lastname: Ortenzi
authors.forename: Peter W
authors.initials: PW
authors.lastname: Dillon
mesh_terms: D000046:Academic Medical Centers; D000293:Adolescent; D000328:Adult; D000368:Aged; D003428:Cross Infection / Q000191:economics / Q000453:epidemiology*; D057286:Electronic Health Records; D005260:Female; D017048:Health Care Costs; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D058996:Quality Improvement; D012189:Retrospective Studies; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000191:economics / Q000453:epidemiology*; D055815:Young Adult