Data elements and validation methods used for electronic surveillance of health care-associated infections: A systematic review

Abstract

Objective This study describes the primary data sources, data elements, and validation methods currently used in electronic surveillance systems (ESS) for identification and surveillance of healthcare-associated infections (HAIs), and compares these data elements and validation methods with recommended standards. Methods Using PRISMA guidelines, a PubMed and manual search was conducted to identify research articles describing ESS for identification and surveillance of HAIs published from January 1, 2009 through August 31, 2014. Selected articles were evaluated to determine what data elements and validation methods were included. Results Among the 509 articles identified in the original literature search, 30 met the inclusion criteria. While the majority of studies (83%) used recommended data sources and validated the numerator (80%), only 10 percent of studies performed external and internal validation. In addition, there was variation in ESS data formats used. Conclusions The findings of this review suggest that the majority of ESS for HAI surveillance are using standard definitions, but the lack of widespread internal data, denominator, and external validation in these systems reduces the reliability of their findings. Additionally, advanced programming skills are required to create, implement and maintain these systems and to reduce the variability in data formats.

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

“…This is making systems more sensitive yet less specific, but also allows systems to be tailored to the needs of healthcare institutes' surveillance programmes. The findings of Cato's review [93], in 2015, suggest that the majority of EASS for HAI surveillance are using standard definitions of HAI, but the lack of standardised use of data formats, denominator, and external validation in these systems reduces the reliability of their findings. Category 1: international classification of diseases coding only; category 2: microbiology (bacterial, viral, fungal pathogens detected by culture, molecular or serological diagnostics); category 3: microbiology + antibiotic prescriptions; category 4: microbiology + antibiotic prescriptions + clinical chemistry; category 5: other types of algorithms (using parameters like body temperature OR/AND judgement by physician OR/AND ventilator setting OR/AND fuzzy logic or natural language processing of clinical notes OR/AND risk factors, like indwelling catheters).…”

Measuring Surgical Site Infections in Children: Comparing Clinical, Electronic, and Administrative Data

“…Caution should still be exercised when interpreting administrative and electronic SSI rates and associated costs for surveillance and reporting purposes. 23,24 The implications of these differences in measurement of SSI are substantial given the controversy surrounding the most favorable data source for measuring postoperative complications for pay-for-performance and public reporting policies. 4,5 A study by Lawson et al…”

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

“…In the field of infection prevention and control, specifically, automated methods of case finding have helped ease the burden of manual data collection and mandatory public reporting to local, state, and federal agencies, allowing clinical staff to focus on other priorities such as education and quality improvement initiatives. Nonetheless, although electronic algorithms have proved valid for some surveillance and research applications such as the identification of bloodstream infections, other types of infection require more nuanced review by experienced clinicians for diagnosis and follow-up (Cato, Cohen, & Larson, 2015). …”