Informatics and Epidemiology in Infection Control

Woeltje, Keith F.; Lautenbach, Ebbing

doi:10.1016/j.idc.2010.11.013

Cited by 9 publications

(10 citation statements)

References 26 publications

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“…[20] Following a development and validation period, fully-automated algorithms require minimal input from infection control staff beyond occasional quality control audits or adjustments to the program due to changing surveillance definitions. Moreover, fully automated systems that search only objective electronic data may virtually eliminate reliance on subjective aspects of surveillance definitions.…”

Section: Discussionmentioning

confidence: 99%

An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units

Hsu¹,

Shenoy²,

Kelbaugh³

et al. 2015

American Journal of Infection Control

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Objective To develop an electronic surveillance tool for catheter-associated urinary tract infections (CAUTIs) and assess its performance. Methods The study was conducted at a 947-bed tertiary care center. Subjects included adults aged ≥18 years, admitted to an intensive care unit (ICU) between January 10 and June 30, 2012 with an indwelling urinary catheter during their admission. We identified CAUTIs using four methods: (1) Traditional Surveillance (TS): manual chart review by Infection Control Practitioners, (2) an Electronic Surveillance (ES) tool, (3) Augmented Electronic Surveillance (AES): ES with chart review on a subset of cases, and (4) Reference Standard (RS): A subset of CAUTIs originally ascertained by TS or ES, confirmed by review. We assessed performance characteristics to RS for reviewed cases. Results We identified 417 candidate CAUTIs in 308 patients; 175 (42.0%) of these candidate CAUTIs were selected for review, yielding 32 confirmed CAUTI in 22 patients (RS). Compared with RS, the sensitivities of TS, ES, and AES were 43.8% (95% confidence interval [CI]: 26.4–62.3%), 100.0% (95% CI: 89.1–100.0%), and 100.0% (89.1–100.0%). Specificities were 82.5% (95% CI: 75.3–88.4%), 2.8% (95% CI: 0.8–7.0%), and 100.0% (95% CI: 97.5–100.0%). Discussion Traditional methods of CAUTI surveillance are error-prone and resource-intensive. We developed a highly sensitive electronic surveillance tool. Conclusion Electronic CAUTI surveillance offers a streamlined approach to improve reliability and resource burden of surveillance.

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Section: Discussionmentioning

confidence: 99%

An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units

Hsu¹,

Shenoy²,

Kelbaugh³

et al. 2015

American Journal of Infection Control

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“…One reason may be the sheer complexity of the systems and the data. 211,215 Other reasons may be human factors such as the ICPs understanding of automated surveillance processes, financial and leadership support, the perception of data as meaningful, and acceptance that some issues cannot be solved by automated systems. …”

Section: -211mentioning

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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“…These sources included: (1) paper-based patient visit surveys (2) billing data (3) nurse telephone logs (4) transcribed telephone calls (5) school records (6) insurance records (7) behavioral health clinic documentation. Combining these different sources of data posed significant challenges in assessing outcomes.…”

Section: Example Casesmentioning

confidence: 99%

“…Trends or patterns in large datasets can increasingly be exposed using data-mining technologies and methods that have been adopted for wide ranging uses in biomedicine. (2, 3) For CER, such technologies and approaches are particularly useful for hypotheses generation, study design and feasibility assessment, and cohort identification activities like participant recruitment.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge Management and Informatics Considerations for Comparative Effectiveness Research

et al. 2013

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Background As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data re-use. Design After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present two research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes. Results The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (e.g. data re-use, data preparation) as well as those related to people and organizational issues (e.g. socio-technical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented. Conclusion Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.

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Informatics and Epidemiology in Infection Control

Cited by 9 publications

References 26 publications

An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units

An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units

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

Knowledge Management and Informatics Considerations for Comparative Effectiveness Research

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