Automating data abstraction in a quality improvement platform for surgical and interventional procedures.
Automating data abstraction in a quality improvement platform for surgical and interventional procedures.
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DOI：
10.13063/2327-9214.1114
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发表时间：
2014-01-01
期刊：
影响因子：
--
通讯作者：
Tarczy-Hornoch, Peter
中科院分区：
文献类型：
--
作者：
Yetisgen, Meliha;Klassen, Prescott;Tarczy-Hornoch, Peter
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.
