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 UK's 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.
@article{morgan2018determining,
title = {Determining patient outcomes from patient letters: {A} comparison
of text analysis approaches},
author = {Morgan, Jennifer and Harper, Paul R. and Knight, Vincent A. and
Nelson, Andrew and Artemiou, Andreas and Carney, Alex},
journal = {Journal of the Operational Research Society},
year = {2018},
doi = {10.1080/01605682.2018.1506559},
url = {https://www.tandfonline.com/doi/full/10.1080/01605682.2018.1506559},
}