Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining
Bibliographic Data
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
Ambulatory · Ambulatory care · Chart · Family medicine · Health care · Identification (biology) · Injury prevention · Medical emergency · Occupational safety and health · Poison control · Statistics · Suicide prevention · Emergency Medicine · Imbalanced Data Classification Techniques · Injury Epidemiology and Prevention · Internal Medicine · Medicine · Trauma and Emergency Care Studies
| Citations per year | 0.14 |
|---|---|
| Citation span | 2019 - 2019 (1) |
| Highly cited | No |