SynthesisBMJ health & care informatics2021
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.
Synthesis in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis.Journal of medical Internet research · 2024Pooled it
- Application of the FRAME-IS to a multifaceted implementation strategy.BMC health services research · 2024Trial
- Embracing Digital as a Paradigm Shift in Medical Affairs.Pharmaceutical medicine · 2026Review
- Introducing a New Research Design: The Multidimensional Interpretative Textual (MIT) Design. Epistemological and Methodological Foundations.Nursing inquiry · 2026Article
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Public Perception of Health Care Before, During, and After COVID-19: Longitudinal Analysis of Online Reviews.Journal of medical Internet research · 2026Observational
- Exploiting survey and online patient experience comments in general practice: validating a fine-tuned language model for automatic sentiment analysis.Family practice · 2026Article
- Understanding online hospital patient reviews: a nationwide cross-sectional analysis of polarity, actionability and themes.BMJ open quality · 2026Article
- The ethical application of artificial intelligence in digital health: patients' knowledge, pulmonary hypertension, and the problem of misrecognition.European heart journal. Digital health · 2026Article
- Online Patient Reviews for Continuous Quality Improvement: Topic Modeling of Hospital Service Quality in Taiwan and the United States.Healthcare (Basel, Switzerland) · 2026Article
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- How Different Medical Practices Are Associated with Types of Patient Complaints in Russian Clinics.Healthcare (Basel, Switzerland) · 2026Article
- Breast Cancer Screening Knowledge and Sentiments in Singaporean Women: Mixed Methods Study Using Topic Modeling, Sentiment Analysis, and Structured Questionnaire Data.Journal of medical Internet research · 2026Article
- From patient voices to policy: Data analytics reveals patterns in Ontario's hospital feedback.PLOS digital health · 2026Article
- Adapting quality function deployment to translate patient feedback into prioritized technical requirements for healthcare artificial intelligence.Scientific reports · 2026Article
- Reddit and rare diseases: what myositis communities tell us about support and struggle.Oxford open digital health · 2026Article
- Automated Evaluation of Reflection and Feedback Quality in Workplace-Based Assessments by Using Natural Language Processing: Cross-Sectional Competency-Based Medical Education Study.JMIR medical education · 2025Article
- From words to action? A scoping review on automatic sentiment analysis of patient experience comments from online sources and surveys.BMJ health & care informatics · 2025Article
- The Use of Natural Language Processing to Interpret Unstructured Patient Feedback on Health Services: Scoping Review.Journal of medical Internet research · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.
methodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.
resultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.
conclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.