title: Automating data abstraction in a quality improvement platform for surgical and interventional procedures.
abstract: OBJECTIVE
  This paper describes a text processing system designed to automate the manual data abstraction process in a quality improvement (QI) program. The Surgical Care and Outcomes Assessment Program (SCOAP) is a clinician-led, statewide performance benchmarking QI platform for surgical and interventional procedures. The data elements abstracted as part of this program cover a wide range of clinical information from patient medical history to details of surgical interventions.
  
  
  METHODS
  Statistical and rule-based extractors were developed to automatically abstract data elements. A preprocessing pipeline was created to chunk free-text notes into its sections, sentences, and tokens. The information extracted in this preprocessing step was used by the statistical and rule-based extractors as features.
  
  
  RESULTS
  Performance results for 25 extractors (14 statistical, 11 rule based) are presented. The average f1-scores for 11 rule-based extractors and 14 statistical extractors are 0.785 (min=0.576,max=0.931,std-dev=0.113) and 0.812 (min=0.571,max=0.993,std-dev=0.135) respectively.
  
  
  CONCLUSIONS
  Our error analysis revealed that most extraction errors were due either to data imbalance in the data set or the way the gold standard had been created.
  
  
  CONCLUSIONS
  As future work, more experiments will be conducted with a more comprehensive data set from multiple institutions contributing to the QI project.
authors.affiliation: University of Washington.
authors.forename: Meliha
authors.initials: M
authors.lastname: Yetisgen
authors.affiliation: University of Washington.
authors.forename: Prescott
authors.initials: P
authors.lastname: Klassen
authors.affiliation: University of Washington.
authors.forename: Peter
authors.initials: P
authors.lastname: Tarczy-Hornoch
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