Mapping Clinical Documents to the Logical Observation Identifiers, Names and Codes (LOINC) Document Ontology using Electronic Health Record Systems Structured Metadata.
AMIA Annu Symp Proc
; 2023: 1017-1026, 2023.
Article
in En
| MEDLINE
| ID: mdl-38222329
ABSTRACT
As Electronic Health Record (EHR) systems increase in usage, organizations struggle to maintain and categorize clinical documentation so it can be used for clinical care and research. While prior research has often employed natural language processing techniques to categorize free text documents, there are shortcomings relative to computational scalability and the lack of key metadata within notes' text. This study presents a framework that can allow institutions to map their notes to the LOINC document ontology using a Bag of Words approach. After preliminary manual value- set mapping, an automated pipeline that leverages key dimensions of metadata from structured EHR fields aligns the notes with the dimensions of the document ontology. This framework resulted in 73.4% coverage of EHR documents, while also mapping 132 million notes in less than 2 hours; an order of magnitude more efficient than NLP based methods.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Logical Observation Identifiers Names and Codes
/
Electronic Health Records
Type of study:
Guideline
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
AMIA Annu Symp Proc
Journal subject:
INFORMATICA MEDICA
Year:
2023
Document type:
Article