Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining
Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review. Results. STM models based on training data from a single facility resulted in accuracy of 87.5% and 87.1%, F-measure of 87.0% and 90.9%, sensitivity of 92.1% and 94.1%, and specificity of 83.6% and 77.8% at the visit and patient levels, respectively. Results from training data from multiple facilities were almost identical. Conclusions. STM has the potential to improve identification of fall-related injuries in the VHA, providing a model for wider application in the evolving national EHR system.
Luther, Stephen L.; McCart, James A.; Berndt, Donald J.; Hahm, Bridget; Finch, Dezon; Jarman, Jay; Foulis, Philip R.; Lapcevic, William A.; Campbell, Robert R.; Shorr, Ronald I.; Valencia, Keryl Motta; and Powell-Cope, Gail. 2015. Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining. American Journal of Public Health. Vol.105(6). 1168-1173. https://doi.org/10.2105/AJPH.2014.302440 PMID: 25880936 ISSN: 0090-0036