ArticleBMC medical informatics and decision making2022
Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- 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
- Enhancing hospital workforce planning, scheduling, and performance evaluation through an AI-driven human resource management system.Scientific reports · 2026Article
- From patient voices to policy: Data analytics reveals patterns in Ontario's hospital feedback.PLOS digital health · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- 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
- Artificial intelligence and the future of patient-centered outcomes.Journal of patient-reported outcomes · 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
- Consumer insights from a feasibility study on remote and extended use of a novel non-invasive wearable fetal electrocardiogram monitor.NPJ digital medicine · 2025Article
- Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study.JMIR medical informatics · 2025Article
- Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study.Journal of medical Internet research · 2025Article
- Enhancing the Utility of Health Related Quality of Life (HRQoL) Assessment Tools in Abdominal Wall Hernia (AWH) Surgery Through Artificial Intelligence (AI): A Framework Proposal.Journal of abdominal wall surgery : JAWS · 2025Article
- Towards a Novel Patient Experience Survey System: Incorporating Physician Perspectives into Performance Feedback Dashboard Design.Journal of patient experience · 2025Article
- Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals.BMJ health & care informatics · 2024Article
- Article
- Deconstructing subjective unmet healthcare needs: a South Korean case study with policy implications.Frontiers in public health · 2024Article
- Insights into the nutritional prevention of macular degeneration based on a comparative topic modeling approach.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEvaluating patients' experiences is essential when incorporating the patients' perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness.
methodsWe developed the Artificial Intelligence Patient-Reported Experience Measures (AI-PREM) tool, consisting of a new, open-ended questionnaire, an NLP pipeline to analyze the answers using sentiment analysis and topic modeling, and a visualization to guide physicians through the results. The questionnaire and NLP pipeline were iteratively developed and validated in a clinical context.
resultsThe final AI-PREM consisted of five open-ended questions about the provided information, personal approach, collaboration between healthcare professionals, organization of care, and other experiences. The AI-PREM was sent to 867 vestibular schwannoma patients, 534 of which responded. The sentiment analysis model attained an F1 score of 0.97 for positive texts and 0.63 for negative texts. There was a 90% overlap between automatically and manually extracted topics. The visualization was hierarchically structured into three stages: the sentiment per question, the topics per sentiment and question, and the original patient responses per topic.
conclusionsThe AI-PREM tool is a comprehensive method that combines a validated, open-ended questionnaire with a well-performing NLP pipeline and visualization. Thematically organizing and quantifying patient feedback reduces the time invested by healthcare professionals to evaluate and prioritize patient experiences without being confined to the limited answer options of closed-ended questions.
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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.