Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection

Objective

To formulate a model for translating manual infection control surveillance methods to automated, algorithmic approaches.

Design

We propose a model for creating electronic surveillance algorithms by translating existing manual surveillance practices into automated electronic methods. Our model suggests that three dimensions of expert knowledge be consulted: clinical, surveillance, and informatics. Once collected, knowledge should be applied through a process of conceptualization, synthesis, programming, and testing.

Results

We applied our framework to central vascular catheter associated bloodstream infection surveillance, a major healthcare performance outcome measure. We found that despite major barriers such as differences in availability of structured data, in types of databases used and in semantic representation of clinical terms, bloodstream infection detection algorithms could be deployed at four very diverse medical centers.

Conclusions

We present a framework that translates existing practice—manual infection detection—to an automated process for surveillance. Our experience details barriers and solutions discovered during development of electronic surveillance for central vascular catheter associated bloodstream infections at four hospitals in a variety of data environments. Moving electronic surveillance to the next level—availability at a majority of acute care hospitals nationwide—would be hastened by the incorporation of necessary data elements, vocabularies and standards into commercially available electronic health records.