Electronic Surveillance of Surgical Site Infections.
Background: Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic...
| Publicado en: | Surgical Infections Vol. 18; no. 4; pp. 498 - 503 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
May/Jun2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123224506&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123224506 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10962964 N3P jtl: Surgical Infections issn: 10962964 maglogo: N pubinfo: dt: May/Jun2017 vid: 18 iid: 4 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 123224506 123224506 NLM28402721 123224506 10.1089/sur.2016.262 NLM28402721 123224506 ppf: 498 ppct: 5 formats: tig: atl: Electronic Surveillance of Surgical Site Infections. aug: au: Cato, Kenrick D. Liu, Jianfang Cohen, Bevin Larson, Elaine affil: School of Nursing, Columbia University, New York, New York. sug: subj: Population Surveillance Methods Algorithms Surgical Wound Infection Epidemiology Human ab: Background: Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic classification algorithm for conservative case finding and identify alterations that would adapt the method for other purposes.Methods: Two computer algorithms were created to identify SSI. One model used a strict National Healthcare Safety Network (NHSN) based SSI algorithm, which was applied to all discharges from 443,284 all discharges from four hospitals in Manhattan, NY, 2009 through 2012. The second model used discharges that only had NHSN-defined SSI procedures during the same period.Results: The strict SSI algorithm was able to classify SSI status for 27.3% of discharges; there was a high number of indeterminate cases. In contrast, the modified, less strict model, classified 97.2% of discharges with NHSN-approved SSI procedures.Conclusion: Electronic records provide several options for aiding with the identification of infections in healthcare settings and can be tailored to suit specific uses. While algorithms for SSI classification should reflect the NHSN definition, our research emphasizes how variations of model building can affect the number of indeterminate cases that may necessitate manual review. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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