ArticleBMC medical informatics and decision making2020
How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- From patient voices to policy: Data analytics reveals patterns in Ontario's hospital feedback.PLOS digital health · 2026Article
- 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
- Using Natural Language Processing to Explore Patient Perspectives on AI Avatars in Support Materials for Patients With Breast Cancer: Survey Study.Journal of medical Internet research · 2025Article
- Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study.JMIR medical informatics · 2025Article
- Exploring the Determinants of Patient Experiences Using the Digital Topic Modeling Approach.Journal of nursing management · 2025Article
- Identifying Patients' Preference During Their Hospital Experience. A Sentiment and Topic Analysis of Patient-Experience Comments via Natural Language Techniques.Patient preference and adherence · 2025Article
- Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.JMIR medical informatics · 2024Article
- Leveraging body-worn camera footage to assess the effects of training on officer communication during traffic stops.PNAS nexus · 2024Article
- Google star ratings of Canadian hospitals: a nationwide cross-sectional analysis.BMJ open quality · 2024Article
- The added value of the artificial intelligence patient-reported experience measure (AI-PREM tool) in clinical practise: Deployment in a vestibular schwannoma care pathway.PEC innovation · 2023Article
- Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.JMIR formative research · 2023Article
- The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing.JMIR medical informatics · 2022Article
- Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).BMC medical informatics and decision making · 2022Article
- Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing.Yearbook of medical informatics · 2021Review
- Categorising patient concerns using natural language processing techniques.BMJ health & care informatics · 2021Article
- Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic.Health and technology · 2021Article
- Perceptions and Responses to Diseases among Patients with Inflammatory Bowel Disease: Text Mining Analysis of Posts on a Japanese Patient Community Website.Inflammatory intestinal diseasesArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement.
methodsThis retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator 'impact', combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital.
resultsA total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator 'impact' revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique.
conclusionsIn both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model's architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information.
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