Manual chart review is labor-intensive and requires specialized knowledge possessed by highly-trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural language processing (NLP) tools are distinctive in their ability to extract critical information from raw text in the electronic health records (EHR). As a simple proof-of-concept, for the potential application of this technology, we examined its ability to discriminate between a binary classification (periprosthetic fracture [PPFFx] vs. no PPFFx) followed by a more complex classification of the same problem (Vancouver). PPFFx were identified among all THAs performed at a single academic institution between 1977 and 2015. A training cohort (n = 90 PPFFx) selected randomly by an electronic program was utilized to develop a prototype NLP algorithm and an additional randomly-selected 86 PPFFx were used to further validate the algorithm. Keywords to identify, and subsequently classify, Vancouver type PPFFx about THA were defined. The algorithm was applied to consult and operative notes to evaluate language used by surgeons as a means to predict the correct pathology in the absence of a listed, precise diagnosis (e.g. Vancouver B2). Validation statistics were calculated using manual chart review as the gold standard.Introduction
Methods
