Paper Info
Title
Determining patient outcomes from patient letters: A comparison of text analysis approaches
Abstract
This paper presents a case study comparing text analysis approaches used to classify the current status of a patient to inform scheduling. It aims to help one of the UKs largest healthcare providers systematically capture patient outcome information following a clinic attendance, ensuring records are closed when a patient is discharged and follow-up appointments can be scheduled to occur within the time-scale required for safe, effective care. Analysing patient letters allows systematic extraction of discharge or follow-up information to automatically update a patient record. This clarifies the demand placed on the system, and whether current capacity is a barrier to timely access. Three approaches for systematic information capture are compared: phrase identification (using lexicons), word frequency analysis and supervised text mining. Approaches are evaluated according to their precision and stakeholder acceptability. Methodological lessons are presented to encourage project objectives to be considered alongside text classification methods for decision support tools.
Year
DOI
Venue
2019
10.1080/01605682.2018.1506559
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
Keywords
Field
DocType
Decision support systems,health service,text mining,information systems
Health care,Information system,Text mining,Computer science,Scheduling (computing),Decision support system,Knowledge management,Health services,Management science
Journal
Volume
Issue
ISSN
70.0
9.0
0160-5682
Citations 
PageRank 
References 
0
0.34
14
Authors
7
Name
Order
Citations
PageRank
Jennifer Sian Morgan1101.71
Jennifer Sian Morgan2101.71
Paul R. Harper318818.44
Vincent A. Knight43710.00
Andreas Artemiou523.15
Alex Carney600.34
Andrew Nelson701.01