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Improving identification of fall-related injuries in ambulatory care using statistical text mining

Author

Listed:
  • Luther, S.L.
  • McCart, J.A.
  • Berndt, D.J.
  • Hahm, B.
  • Finch, D.
  • Jarman, J.
  • Foulis, P.R.
  • Lapcevic, W.A.
  • Campbell, R.R.
  • Shorr, R.I.
  • Valencia, K.M.
  • Powell-Cope, G.

Abstract

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. © 2015, American Public Health Association Inc. All rights reserved.

Suggested Citation

  • Luther, S.L. & McCart, J.A. & Berndt, D.J. & Hahm, B. & Finch, D. & Jarman, J. & Foulis, P.R. & Lapcevic, W.A. & Campbell, R.R. & Shorr, R.I. & Valencia, K.M. & Powell-Cope, G., 2015. "Improving identification of fall-related injuries in ambulatory care using statistical text mining," American Journal of Public Health, American Public Health Association, vol. 105(6), pages 1168-1173.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2014.302440_0
    DOI: 10.2105/AJPH.2014.302440
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    Cited by:

    1. Berit I. Helgheim & Rui Maia & Joao C. Ferreira & Ana Lucia Martins, 2019. "Merging Data Diversity of Clinical Medical Records to Improve Effectiveness," IJERPH, MDPI, vol. 16(5), pages 1-20, March.

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