The electronic medical record as a tool for infection surveillance: Successful automation of device-days

Wright, Marc‐Oliver; Fisher, Adrienne; John, Maria Sindhura; Reynolds, Kate; Peterson, Lance R.; Robicsek, Ari

doi:10.1016/j.ajic.2008.11.003

Cited by 48 publications

(32 citation statements)

References 12 publications

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“…30,31 As in model development, the use of routine care data was associated with missing predictor values; a larger fraction of patients had missing values in hospitals B and C. Multiple imputation was a feasible method of handling these missing observations in this context, and future work will need to identify the optimal method of applying the model in practice. 32 Interestingly, as a result of daily CSF sampling in hospital A, 83.3% of patients had no missing predictor information but model efficiency was lower compared to the other hospitals.…”

Section: Discussionmentioning

confidence: 99%

Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study

Mourik

Troelstra

Sprenkel

et al. 2015

Infect. Control Hosp. Epidemiol.

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objective. Manual surveillance of healthcare-associated infections is cumbersome and vulnerable to subjective interpretation. Automated systems are under development to improve efficiency and reliability of surveillance, for example by selecting high-risk patients requiring manual chart review. In this study, we aimed to validate a previously developed multivariable prediction modeling approach for detecting drain-related meningitis (DRM) in neurosurgical patients and to assess its merits compared to conventional methods of automated surveillance.methods. Prospective cohort study in 3 hospitals assessing the accuracy and efficiency of 2 automated surveillance methods for detecting DRM, the multivariable prediction model and a classification algorithm, using manual chart review as the reference standard. All 3 methods of surveillance were performed independently. Patients receiving cerebrospinal fluid drains were included (2012)(2013), except children, and patients deceased within 24 hours or with pre-existing meningitis. Data required by automated surveillance methods were extracted from routine care clinical data warehouses.results. In total, DRM occurred in 37 of 366 external cerebrospinal fluid drainage episodes (12.3/1000 drain days at risk). The multivariable prediction model had good discriminatory power (area under the ROC curve 0.91-1.00 by hospital), had adequate overall calibration, and could identify high-risk patients requiring manual confirmation with 97.3% sensitivity and 52.2% positive predictive value, decreasing the workload for manual surveillance by 81%. The multivariable approach was more efficient than classification algorithms in 2 of 3 hospitals.conclusions. Automated surveillance of DRM using a multivariable prediction model in multiple hospitals considerably reduced the burden for manual chart review at near-perfect sensitivity.Infect Control Hosp Epidemiol 2015;36(1): [65][66][67][68][69][70][71][72][73][74][75] introduction Electronically collected routine care data are increasingly employed to meet growing demands for reliable and timely information on healthcare-associated infection (HAI) rates. [1][2][3] For several decades, surveillance of and feedback regarding HAI rates, for example within national networks, have been fundamental components of infection prevention programs. [4][5][6][7] Traditionally, surveillance is performed by infection preventionists who manually review patient charts for the occurrence of targeted HAIs. This approach, however, is known to be labor intensive, effort dependent, and vulnerable to subjective interpretation. [8][9][10][11] Expansion of surveillance volume requirements and public reporting of HAI rates has stimulated the use of (semi)automated systems that combine various sources of data captured in electronic health records (EHRs) to support or replace manual surveillance. 12,13 Most of these automated surveillance systems aim to classify patients by their likelihood of having developed the targeted HAI and thereby restrict manual chart review to...

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Section: Discussionmentioning

confidence: 99%

Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study

Mourik

Troelstra

Sprenkel

et al. 2015

Infect. Control Hosp. Epidemiol.

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“…In the CAUTI context , the capacity to collect patient-days as units is vital fro m the standpoint of measuring device utilizat ion. Furthermore, it is also impo rtant when alternate denominators become standard [14].…”

Section: ) Urinary Tract Infections (Utis)mentioning

confidence: 99%

“…It is no longer possible to report asymptomatic infections that have no secondary bacteremia whereas the rates of infection appear in reports as symptomatic UTIs for every 1,000 device -days (catheter) [26]. To determine the deno minator data, the process resembles the CVC device-days although electronic urinary device-days' capture needs explicit documenting within the electronic system [14]. To allow for semi-and fully-auto mated surveillance, device data at individual patient levels is crucial.…”

Section: ) Urinary Tract Infections (Utis)mentioning

confidence: 99%

“…Ventilators have the capacity to transmit data electronically with accurate informat ion concerning the exposures of patients. Surveillance systems with the capacity of acco mmodating device data in electronic form could enhance denominator computations' correctness in addition to its efficiency [14]. Lastly, the capacity to capture the days of patients by unit is essential when determining the rates of devicerelated rates [14].…”

Section: ) Denominator Data Capturingmentioning

confidence: 99%

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Development of Infection Control Surveillance System for Intensive Care Unit: Data Requirements and Guidelines

Aljohani¹,

Nadeem²,

Abumelha³

et al. 2016

IJISA

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Abstract-Surveillance systems are useful in the identification of patients that contract infections during their hospitalization period. Despite still being at in fancy, electronic information control surveillance systems for Hospital Acquired Infect ions (HAIs) are imp roving and becoming more co mmonplace as the acceptance levels rise. There are crucial gaps in existing knowledge concerning the best ways for imp lementing electronic surveillance systems especially in the context of the Intensive Care Unit (ICU). To bridge this gap, the aim of this paper was to provide a comprehensive review of various electronic surveillance approaches and to highlight the requisite data components and offer guidelines. This rev iew revealed denominator, nu merator, and discrete data requirements and guidelines for the surveillance of four main ICU HAIs, including Central Line-Associated Bloodstream Infection (CLABSI), Urinary Tract Infect ion (UTI), Surgical Site Infect ions (SSIs) and Ventilator-Associated Conditions/Events (VACs/VAEs).

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“…Wide-scale adoption of the computer algorithm in a consistent and verifiable manner across reporting sites will be feasible in the future as clinical data become standardized. 11,24 Healthcare and public health leaders should continue to support capacity building and further evaluation of automated methods of healthcareassociated infection detection.…”

Section: Relationship Between Performance (Audit Agreement) and Centrmentioning

confidence: 99%

Multicenter Evaluation of Computer Automated versus Traditional Surveillance of Hospital-Acquired Bloodstream Infections

Lin

Woeltje

Khan

et al. 2014

Infect. Control Hosp. Epidemiol.

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The electronic medical record as a tool for infection surveillance: Successful automation of device-days

Cited by 48 publications

References 12 publications

Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study

Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study

Development of Infection Control Surveillance System for Intensive Care Unit: Data Requirements and Guidelines

Multicenter Evaluation of Computer Automated versus Traditional Surveillance of Hospital-Acquired Bloodstream Infections

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