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Submission Type

Empirical Research

Keywords

Natural language processing, Quality improvement, SCOAP CERTAIN

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.

Creative Commons License


This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1114

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