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

José Carlos Ferrão, Monica Oliveira, Filipe Janela, Henrique M. G. Martins and Daniel Gartner

Health Systems, 2021, vol. 10, issue 2, 138-161

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/20476965.2020.1729666 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:thssxx:v:10:y:2021:i:2:p:138-161

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/thss20

DOI: 10.1080/20476965.2020.1729666

Access Statistics for this article

Health Systems is currently edited by Sally Brailsford

More articles in Health Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().