Using artificial intelligence (AI) for sentiment analysis of patient-reported outcomes (PROs) is shaping the interpretation of patient voice in the healthcare systems by revolutionizing free-text narratives into systematic signals about emotion, burden, and lived experience. While conventional PRO instruments offer standard measurements, they often condense complex experiences into fixed scales. AI-driven natural language processing (NLP) enables systematic analysis of patient narratives captured through open-ended responses, diaries, and digital tools, thus enhancing understanding beyond statistical scores.(1, 2)
Sentiment analysis identifies patient language by emotional tone and intensity and associates those emotions to particular elements of care, such as symptoms, communication, or treatment burden. This approach supports usual PRO measures (PROMs) and patient-reported experience measures (PREMs) by adding emotional and contextual gravity, showing how patients experience care rather than just how they rate it. Consequently, sentiment-derived insights can help explain why similar PRO scores may reflect very different patient experiences.(3, 4)
Sentiment analysis has an important ability to reveal dissatisfaction and distress that may be masked by ceiling effects in standard rating measures. Patients usually report high scores while reporting important concerns in free-text comments, especially around communication and care coordination. NLP-enabled sentiment parameters can distinguish between these details to offer more actionable insight into areas requiring most improvements.(2-4)
Machine learning developments have enhanced the accuracy of sentiment analysis in healthcare, with transformer-based models adjusted to patient and clinical language. These models are often better than rule-based approaches at capturing context, politeness, and domain-specific terminology, facilitating more consistent identification of subtle emotions, like anxiety, frustration, or disengagement. Therefore, sentiment analysis has become more reliable for use in research and routine care settings.(5, 6)
Integrating AI-driven sentiment analysis into PRO workflows can support earlier detection of deterioration of quality of life, emotional distress, or disengagement from treatment. Monitoring changes in sentiment over time enables clinicians and health systems to determine emerging issues before they appear in conventional indicators, supporting more timely, patient-centred interventions and quality improvement efforts.(1, 2, 4)
In practice, AI-driven PRO and PREM systems increasingly combine open-text collection with automated sentiment analysis and visualisation to guide clinical team. Evidence from large patient-experience datasets shows that sentiment scores derived from narratives can be linked meaningfully with validated quality measures, highlighting their value as complementary indicators of care quality.(1, 7)
Simultaneously, responsible use of sentiment analysis needs careful attention to bias, transparency, and governance. Models may misread culturally specific language or underrepresent certain patient groups, highlighting the need for diverse training data, validation against human judgement, and clear communication about the analysis of patient narratives. Sentiment analysis should complement, not replace, clinical interpretation.(8)
Largely, integrating AI-enabled sentiment analysis as a continuous listening layer within digital PRO systems provides a scalable way to capture not only the scores but also the experiences of patients. When applied carefully and assessed rigorously, this approach can support patient-centred evidence generation and strengthen care decisions to better reflect patients’ real-world experiences.
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References
- van Buchem MM, Neve OM, Kant IMJ, et al. Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM). BMC Med Inform Decis Mak. 2022 Jul 15;22(1):183.
- Khanbhai M, Warren L, Symons J, et al. Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care. International Journal of Medical Informatics. 2022; 157:104642.
- Wójcik Z, Dimitrova V, Warrington L, et al. Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review. Health Qual Life Outcomes. 2025 Apr 11;23(1):37.
- Azarpey A, Thomas J, Ring D, Franko O. Natural Language Processing of Sentiments Identified in Patient Comments Associated with Less Than Top-Rated Care. J Patient Exp. 2025 Mar 21;12:23743735251323677.
- Nerella S, Bandyopadhyay S, Zhang J, et al. Transformers and large language models in healthcare: A review. Artif Intell Med. 2024 Aug;154:102900.
- Chaudhary A, Pokhrel S, Ganesan S, Shah PB. Drug Review Sentiment Analysis: Applying Transformer-Based Models for Enhanced Healthcare. Journal of Data Science and Intelligent Systems. 2025; 1-11.
- Greaves F, Ramirez-Cano D, Millett C, et al. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res. 2013 Nov 1;15(11):e239.
- Saha S. Advancements In Sentiment Analysis: Techniques, Applications, And Future Directions. IJCSPUB. 2024; 14(4):126-159.