Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk.
about
Quality of data reported to a smaller-hospital pilot surveillance program.Validation of coronary artery bypass graft surgical site infection surveillance data from a statewide surveillance system in Australia.Why are we trying to reduce length of stay? Evaluation of the costs and benefits of reducing time in hospital must start from the objectives that govern change.Monitoring expert system performance using continuous user feedbackOutbreak of severe Pseudomonas aeruginosa infections caused by a contaminated drain in a whirlpool bathtub.Trends in antifungal use and epidemiology of nosocomial yeast infections in a university hospital.The impact of hospital-acquired bloodstream infections.Automated methods for surveillance of surgical site infections.Effect of serum on the in vitro activities of 11 broad-spectrum antibiotics.Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy.Investigation of Candida albicans transmission in a surgical intensive care unit cluster by using genomic DNA typing methods.Validation of surgical site infection surveillance in the Netherlands.Challenges in reporting surgical site infections to the national surgical site infection surveillance and suggestions for improvementStructure, Process, and Outcome Quality of Surgical Site Infection Surveillance in Switzerland.Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections.Factors and Costs Associated With Delay in Treatment Initiation and Prolonged Length of Stay With Inpatient EPOCH Chemotherapy in Patients With Hematologic Malignancies.Basics of surgical-site infection surveillance.Identifying meaningful outcome measures for the intensive care unit.Sampling for collection of central line-day denominators in surveillance of healthcare-associated bloodstream infections.Validation of surveillance in the intensive care unit component of the German nosocomial infections surveillance system.How many nosocomial infections are missed if identification is restricted to patients with either microbiology reports or antibiotic administration?
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P2860
Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk.
description
article científic
@ca
article scientifique
@fr
articol științific
@ro
articolo scientifico
@it
artigo científico
@gl
artigo científico
@pt
artigo científico
@pt-br
artikel ilmiah
@id
artikull shkencor
@sq
artículo científico
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name
Nosocomial infections: validat ...... to identify patients at risk.
@en
Nosocomial infections: validat ...... to identify patients at risk.
@nl
type
label
Nosocomial infections: validat ...... to identify patients at risk.
@en
Nosocomial infections: validat ...... to identify patients at risk.
@nl
prefLabel
Nosocomial infections: validat ...... to identify patients at risk.
@en
Nosocomial infections: validat ...... to identify patients at risk.
@nl
P2093
P1476
Nosocomial infections: validat ...... to identify patients at risk.
@en
P2093
P304
P356
10.1093/OXFORDJOURNALS.AJE.A115558
P407
P577
1990-04-01T00:00:00Z