Determining patient outcomes from patient letters: A comparison of text analysis approaches. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Morgan, Jennifer; Harper, Paul; Knight, Vincent; Artemiou, Andreas; Carney, Alex; Nelson, Andrew
• Is Part Of: Journal of the Operational Research Society. Volume 70:Issue 9(2019)
• Page Start: 1425
• Page End: 1439
• Publication Date: 2019-09-02
• Subjects: Decision support systems -- health service -- text mining -- information systems; Operations research -- Periodicals; 658.4034
• DOI: 10.1080/01605682.2018.1506559
• Languages: English
• ISSNs: 0160-5682

Abstract

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.