title: Efficient identification of postdischarge surgical site infections: use of automated pharmacy dispensing information, administrative data, and medical record information.
abstract: Although most surgical site infections (SSIs) occur after hospital discharge, there is no efficient way to identify them. The utility of automated claims and electronic medical record data for this purpose was assessed in a cohort of 4086 nonobstetric procedures following which 96 postdischarge SSIs occurred. Coded diagnoses, tests, and treatments were assessed by use of recursive partitioning, with 10-fold cross-validation, and logistic regression with bootstrap resampling. Specific codes and combinations of codes identified a subset of 2% of all procedures among which 74% of SSIs had occurred. Accepting a specificity of 92% improved the sensitivity from 74% to 92%. Use of only hospital discharge diagnosis codes plus pharmacy dispensing data had sensitivity of 77% and specificity of 94%. All of these performance characteristics were better than questionnaire responses from patients or surgeons. Thus, information routinely collected by health care systems can be the basis of an efficient, largely passive, surveillance system for postdischarge SSIs.
authors.affiliation: Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA. ksands@bidmc.harvard.edu
authors.forename: K
authors.initials: K
authors.lastname: Sands
authors.forename: G
authors.initials: G
authors.lastname: Vineyard
authors.forename: J
authors.initials: J
authors.lastname: Livingston
authors.forename: C
authors.initials: C
authors.lastname: Christiansen
authors.forename: R
authors.initials: R
authors.lastname: Platt
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D003695:Delivery of Health Care; D006801:Humans; D016347:Medical Records Systems, Computerized; D010351:Patient Discharge; D012044:Regression Analysis; D013530:Surgical Wound Infection / Q000145:classification / Q000175:diagnosis*