JMIR Publications

ABSTRACT

Background:

Unstructured patient feedback (UPF) allows patients to freely express their experiences without the constraints of predefined questions. The proliferation of online healthcare rating websites has created a vast source of UPF. Natural language processing (NLP) techniques, particularly sentiment analysis and topic modelling, are increasingly being used to analyse UPF in healthcare settings, however the scope and clinical relevance of these technologies is unclear.

Objective:

This scoping review investigates how NLP techniques are being used to interpret UPF, with focus on the healthcare settings in which this is used, the purposes for using these technologies, and any impacts reported on clinical practice.

Methods:

Searches of the MEDLINE, EMBASE, CINAHL, Cochrane Database of Reviews, and Google Scholar were conducted in February 2024. No date limits were applied. English language studies that used NLP techniques on UPF that pertained to an identifiable health care setting or provider were included. Data extraction focused on the healthcare setting, NLP methods used, and applications of these techniques.

Results:

52 studies were included. NLP was most commonly applied to UPF from secondary care settings (n=33) with fewer in primary (n=10) or community (n=5) care. Three NLP techniques were identified in the included studies: sentiment analysis (n=32), topic modelling (n=15) and text classification (n=7). Sentiment analysis was applied to explore associations between patient sentiment and healthcare provider characteristics, track emotional responses over time, and identify areas for improvement in healthcare delivery. Topic modelling, primarily using Latent Dirichlet Allocation (LDA) algorithm, was employed to uncover latent themes in patient feedback, compare patient experiences across different healthcare settings, and track changes in patient concerns over time. Text classification was used to categorize patient feedback into predefined topics. The association between NLP-derived insights and traditional healthcare quality metrics was limited, with few studies describing concrete clinical impacts resulting from their analyses.

Conclusions:

NLP has been applied to UPF across a number of contexts, primarily to identify features of health services or professionals that support good patient experience. The growth of research publications demonstrates an academic interest in these technologies, but there is little evidence these approaches are being employed in clinical settings. Future research is required to assess how NLP may capture the nuance of healthcare interactions, align with existing quality metrics and how it may be used to influence clinician behaviour