Automatic case cluster detection using hospital electronic health record data

Abstract

Case detection through contact tracing is a key intervention during an infectious disease outbreak. However, contact tracing is an intensive process where a given contact tracer must locate not only confirmed cases, but identify and interview known contacts. Often these data are manually recorded. During emerging outbreaks, the numbers of contacts could expand rapidly and beyond this, when focused on individual transmission chains, larger patterns may not be identified. Understanding if particular cases can be clustered and linked to a common source can help to prioritize contact tracing effects and understand underlying risk factors for large spreading events. Electronic health records systems are used by the vast majority of private health care systems across the United States, providing a potential way to automatically detect outbreaks and connect cases through already collected data. In this analysis we propose an algorithm to identify case clusters within a community during an infectious disease outbreak using Bayesian probabilistic case linking and explore how this approach could supplement outbreak responses; especially when human contact tracing resources are limited.