Can structured EHR data support clinical coding? A data mining approach

被引:15
作者
Ferrao, Jose Carlos [1 ]
Oliveira, Monica Duarte [1 ]
Janela, Filipe [2 ]
Martins, Henrique M. G. [3 ]
Gartner, Daniel [4 ]
机构
[1] Univ Lisbon, Inst Super Tecn, Ctr Management Studies, CEG IST, Lisbon, Portugal
[2] SIEMENS Healthineers, Invest Desenvolvimento & Inovacao, Amadora, Portugal
[3] Hosp Prof Doutor Fernando Fonseca, Ctr Res & Creat Informat CI2, Amadora, Portugal
[4] Cardiff Univ, Sch Math, Cardiff, S Glam, Wales
关键词
Clinical coding; analytics; data mining; decision support; health systems improvement; ELECTRONIC HEALTH RECORD; OPERATIONS-RESEARCH; FEATURE-SELECTION; DISCHARGE SUMMARIES; DIAGNOSIS CODES; CLASSIFICATION; INFORMATION; RELEVANCE; SYSTEM; DEPENDENCY;
D O I
10.1080/20476965.2020.1729666
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.
引用
收藏
页码:138 / 161
页数:24
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