Surveillance of hospital-acquired infections based on electronic hospital registries

Leth, Rita Andersen; Frimodt‐Møller, Niels

doi:10.1016/j.jhin.2005.04.002

Cited by 51 publications

(40 citation statements)

References 19 publications

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“…[11][12][13][14] However, most institutions have focused on CLABSIs, because these seem more amenable to prevention interventions. The study by Trick et al 6 provided a solid step toward performing automated surveillance for CLABSI, but those investigators did not have access to central line data electronically.…”

Section: Discussionmentioning

confidence: 99%

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Woeltje

McMullen

Butler

et al. 2011

Infect. Control Hosp. Epidemiol.

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"Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit." Infection Control and Hospital Epidemiology.32,11. 1086-1090. (2011 http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. The University of Chicago Press and The Society for Healthcare Epidemiology of America are collaborating with JSTOR to digitize, preserve and extend access to Infection Control and Hospital Epidemiology.http://www.jstor.org infection control and hospital epidemiology november 2011, vol. 32, no. 11 o r i g i n a l a r t i c l e background. Manual surveillance for central line-associated bloodstream infections (CLABSIs) by infection prevention practitioners is time-consuming and often limited to intensive care units (ICUs). An automated surveillance system using existing databases with patientlevel variables and microbiology data was investigated.

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Section: Discussionmentioning

confidence: 99%

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Woeltje

McMullen

Butler

et al. 2011

Infect. Control Hosp. Epidemiol.

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show abstract

“…Twenty-five studies involved academic institutions, three involved community hospitals and five studies were performed in a mixed community and academic setting. A thematic abundance of ESS systems was identified, covering a wide spectrum of clinical areas encompassing acute lung injury [13][14][15], patient-ventilator interaction [16][17][18], identification of seizures [19], rapid patient deterioration [20] nosocomial infection surveillance [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], heart failure [36], life-threatening electrocardiographic changes [37][38][39] and hemodynamic stability monitoring [40][41][42].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective:To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

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“…Although traditional case-finding by ICPs has been considered the "gold standard" of HAI surveillance, several newer studies show that this is not necessarily the case: computer-aided surveillance may be more time-efficient and superior in case-finding. Many studies have compared traditional surveillance by ICPs with case-finding using surveillance definitions to available data sources such as ICD-9 diagnostic codes and other administrative coding; 78,81,87,89,[177][178][179][180][181] microbiology, laboratory or pharmacy data; [182][183][184][185] or a combination of these 84,137,146,184,[186][187][188][189] . Some have utilized advanced algorithms to detect SSIs and validate these against ICP case-finding.…”

Section: Positive Aspects Of Automated Case-findingmentioning

confidence: 99%

Methodology of the Norwegian Surveillance System for Healthcare-Associated Infections: The value of a mandatory system, automated data collection, and active postdischarge surveillance

Løwer

Eriksen

Aavitsland

et al. 2013

American Journal of Infection Control

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Surveillance of hospital-acquired infections based on electronic hospital registries

Cited by 51 publications

References 19 publications

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Methodology of the Norwegian Surveillance System for Healthcare-Associated Infections: The value of a mandatory system, automated data collection, and active postdischarge surveillance

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