Empirical research
Automating Data Abstraction in a Quality Improvement Platform for Surgical and Interventional Procedures
Authors:
Meliha Yetisgen ,
University of Washington - Seattle Campus
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.
Findings: 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.
Discussion: 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.
Conclusion: As future work, more experiments will be conducted with a more comprehensive data set from multiple institutions contributing to the QI project.
How to Cite:
Yetisgen M, Klassen P, Tarczy-Hornoch P. Automating Data Abstraction in a Quality Improvement Platform for Surgical and Interventional Procedures. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2014;2(1):17. DOI: http://doi.org/10.13063/2327-9214.1114
Published on
26 Nov 2014.
Peer Reviewed
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