title: Natural language processing to extract follow-up provider information from hospital discharge summaries.
abstract: OBJECTIVE
  We evaluate the performance of a Natural Language Processing (NLP) application designed to extract follow-up provider information from free-text discharge summaries at two hospitals.
  
  
  RESULTS
  We compare performance by the NLP application, called the Regenstrief EXtracion tool (REX), to performance by three physician reviewers at extracting follow-up provider names, phone/fax numbers and location information. Precision, recall, and F-measures are reported, with 95% CI for pairwise comparisons.
  
  
  RESULTS
  Of 556 summaries with follow-up information, REX performed as follows in precision, recall, F-measure respectively: Provider Name 0.96, 0.92, 0.94; Phone/Fax 0.99, 0.92, 0.96; Location 0.83, 0.82, 0.82. REX was as good as all physician-reviewers in identifying follow-up provider names and phone/fax numbers, and slightly inferior to two physicians at identifying location information. REX took about four seconds (vs. 3-5 minutes for physician-reviewers) to extract follow-up information.
  
  
  CONCLUSIONS
  A NLP program had physician-like performance at extracting provider follow-up information from discharge summaries.
authors.affiliation: Regenstrief Institute Inc, Indianapolis, IN and.
authors.forename: Martin C
authors.initials: MC
authors.lastname: Were
authors.forename: Sergey
authors.initials: S
authors.lastname: Gorbachev
authors.forename: Jason
authors.initials: J
authors.lastname: Cadwallader
authors.forename: Joe
authors.initials: J
authors.lastname: Kesterson
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Li
authors.forename: J Marc
authors.initials: JM
authors.lastname: Overhage
authors.forename: Jeff
authors.initials: J
authors.lastname: Friedlin
mesh_terms: D057286:Electronic Health Records; D005500:Follow-Up Studies; D006801:Humans; D009323:Natural Language Processing; D010351:Patient Discharge