科研之友
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摘要
Health care providers, quality advocates, consumers, and legislators are increasingly focused on the prevention of health care-associated infections. Accurate surveillance is essential to identify areas for improvement and to measure the impact of infection prevention initiatives. Conventional surveillance definitions, however, are complicated, costly to apply, and prone to both intentional and unintentional misclassification. Algorithmic analysis of electronic health data is a promising alternative to conventional surveillance. Algorithms that seek combinations of diagnosis codes, microbiological analysis results, and/or antimicrobial dispensing can identify health care-associated infections with sensitivities and positive predictive values that often match or surpass those of conventional surveillance. The efficiency and objectivity of these methods make them promising candidates for more manageable and meaningful benchmarking within and between facilities.
