title: Improving identification of fall-related injuries in ambulatory care using statistical text mining.
abstract: OBJECTIVE
  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.
authors.affiliation: Stephen L. Luther, James A. McCart, Bridget Hahm, Dezon Finch, Philip R. Foulis, William A. Lapcevic, Robert R. Campbell, and Gail Powell-Cope are with the HSR&D Center of Innovation on Disability and Rehabilitation Research, James A. Haley Veterans Hospital, Tampa, FL. Donald J. Berndt is with the University of South Florida College of Business Administration, Tampa. Jay Jarman is with the East Tennessee State University Department of Computing, Johnson City. Ronald I. Shorr is with the North Florida/South Georgia Veterans Health System, Gainesville, FL. Keryl Motta Valencia is with the VA Caribbean Healthcare System, San Juan, PR.
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Luther
authors.forename: James A
authors.initials: JA
authors.lastname: McCart
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Berndt
authors.forename: Bridget
authors.initials: B
authors.lastname: Hahm
authors.forename: Dezon
authors.initials: D
authors.lastname: Finch
authors.forename: Jay
authors.initials: J
authors.lastname: Jarman
authors.forename: Philip R
authors.initials: PR
authors.lastname: Foulis
authors.forename: William A
authors.initials: WA
authors.lastname: Lapcevic
authors.forename: Robert R
authors.initials: RR
authors.lastname: Campbell
authors.forename: Ronald I
authors.initials: RI
authors.lastname: Shorr
authors.forename: Keryl Motta
authors.initials: KM
authors.lastname: Valencia
authors.forename: Gail
authors.initials: G
authors.lastname: Powell-Cope
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care; D000555:Ambulatory Care Information Systems; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011647:Puerto Rico / Q000453:epidemiology; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs