ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Understanding patient satisfaction with received healthcare services: A natural language processing approach.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.BMJ health & care informatics · 2021Pooled it
- Topic-Aware Summarization of Lived Health Care Experiences: Large Language Model Evaluation Study.JMIR medical informatics · 2026Article
- Automated Health Care Messages and Unexpected Patient Responses.JAMA network open · 2026Article
- How Different Medical Practices Are Associated with Types of Patient Complaints in Russian Clinics.Healthcare (Basel, Switzerland) · 2026Article
- From patient voices to policy: Data analytics reveals patterns in Ontario's hospital feedback.PLOS digital health · 2026Article
- 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
- Real-world insights of patient voices with age-related macular degeneration in the Republic of Korea and Taiwan: an AI-based Digital Listening study by Semantic-Natural Language Processing.BMC medical informatics and decision making · 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
- Article
- Opportunities for the use of large language models in hepatology.Clinical liver disease · 2023Article
- 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
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Analysis of a national response to a White House directive for ending veteran suicide.Health services research · 2022Article
- Artificial intelligence technologies and compassion in healthcare: A systematic scoping review.Frontiers in psychology · 2022Article
- Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier.Healthcare (Basel, Switzerland) · 2021Article
- Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook.Healthcare (Basel, Switzerland) · 2021Article
- Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews.International journal of environmental research and public health · 2021Article
- Using Text Mining Techniques to Identify Health Care Providers With Patient Safety Problems: Exploratory Study.Journal of medical Internet research · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments. Our annotation scheme consisted of 28 topics, with positive and negative sentiment. Within those 28 topics, the seven most frequent accounted for 63% of annotations. For automated topic classification, we developed vocabulary-based and Naive Bayes ' classifiers. For sentiment analysis, another Naive Bayes ' classifier was used. Finally, we used topic modeling to search for unexpected topics within negative comments. The seven most common topics were appointment access, appointment wait, empathy, explanation, friendliness, practice environment, and overall experience. The best F-measures from our classifier were 0.52(NB), 0.57(NB), 0.36(Vocab), 0.74(NB), 0.40(NB), and 0.44(Vocab), respectively. F- scores ranged from 0.16 to 0.74. The sentiment classification F-score was 0.84. Negative comment topic modeling revealed complaints about appointment access, appointment wait, and time spent with physician.
Indexed as
Identifiers
28269848PMC5333198What OpenQuestion holds
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.