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  • AI-driven Sentiment Analysis of Patient-Reported Outcomes

    AI-driven Sentiment Analysis of Patient-Reported Outcomes

    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

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Nerella S, Bandyopadhyay S, Zhang J, et al. Transformers and large language models in healthcare: A review. Artif Intell Med. 2024 Aug;154:102900.
    6. 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.
    7. 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.
    8. Saha S. Advancements In Sentiment Analysis: Techniques, Applications, And Future Directions. IJCSPUB. 2024; 14(4):126-159.

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  • The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    Market access has evolved into a multidisciplinary strategy and is no longer confined to traditional pricing and reimbursement. It ensures rapid and sustained availability of therapies to the right patients at the right price.(1) As market access continues to evolve and become more dynamic with rising healthcare costs and growing stakeholder diversity, pharmaceutical companies need to look beyond conventional tools. Sentiment analysis (SA), a branch of social media listening (SML) that uses natural language processing (NLP) to classify texts based on opinion-based or emotional content, offers a compelling solution. SA enables early detection of mindset shifts in perception of treatments by mining real-time data from digital platforms.(2,3)

    SA or opinion mining works by classifying text to detect the contextual polarity of the written content as positive, neutral, or negative.(2) Apart from social media which contributes most data for SA, data is also extracted from other sources such as news articles, blogs, forums, or even clinical documents.(3) In pharmaceutical industry, SA provides early insights into how treatments or disease areas are perceived by patients, clinicians and policymakers. It captures real time emotions, doubts and hidden support which are crucial to anticipate product uptake and shaping market access decisions.(4)

    SA has various applications in early market access planning. It can enable competitive intelligence by analysing stakeholder sentiment around drug launches of competitive interventions, revealing enthusiasm or scepticism. It can help in stakeholder mapping by identifying early adopters, digital key opinion leaders (KOLs), and patient advocates, by reading their online presence. It can also detect pricing and reimbursement cues through reactions from patients and payers through policy pushback, public outrage, or unexpected support. Additionally, SA can gauge regulatory and HTA sentiment by analysing tone in consultations, policy drafts, or legislative debates, providing early advantage in navigating access challenges.(1,2)

    SA is already extensively utilized in the real-world setting in the healthcare industry. For instance, online scepticism delayed biosimilar uptake in parts of Europe despite having a regulatory approval. Feedback was captured through clinician commentary and forums.(2) SA has also been used to detect adverse drug reactions, offering earlier risk signals which may not be captured through official surveillance systems.(5,6) SA can add geographic sensitivity revealing how messaging can be pivoted or economic models can be adjusted across countries or subpopulations. This supports early stakeholder engagement in launch planning.(4)

    Despite the promising scope, SA has a few limitations including bias in source data due to overrepresentation of digitally active users, giving skewed insights.(2) Also, sentiment does not always reflect the behaviour or intent, requiring triangulation with other research methods. Ethically, SA needs to responsibly use public content ensuring transparency and privacy.(2)

    In conclusion, SA provides insight into real-world perceptions that influence access outcomes and can be used to transform market access strategies. By understanding early signs of resistance or unmet needs, SA can enable planning and better alignment with key stakeholders. In an increasingly digital healthcare environment, market access teams should embrace SA as a strategic monitoring tool to strengthen early-phase decision-making to ensure long term success.

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    References:

    1. Kumar A, Juluru K, Thimmaraju PK, Reddy J, Patil A. Pharmaceutical market access in emerging markets: concepts, components, and future. J Mark Access Health Policy. 2014 Dec 1;2.
    2. Sharma C, Whittle S, Haghighi PD, Burstein F, Keen H. Sentiment analysis of social media posts on pharmacotherapy: A scoping review. Pharmacol Res Perspect. 2020 Oct;8(5):e00640.
    3. Devika MD, Sunitha C, Ganesha A. Sentiment analysis: A comparative study on different approaches. Procedia Computer Science 2016;87:44–49.
    4. Sorayaie Azar A, Babaei Rikan S, Naemi A, Bagherzadeh Mohasefi J, Wiil UK. Predicting patients’ sentiments about medications using artificial intelligence techniques. Sci Rep. 2024 Dec 30;14(1):31928.
    5. Tricco AC, Zarin W, Lillie E, Jeblee S, Warren R, Khan PA, Robson R, Pham B, Hirst G, Straus SE. Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC Med Inform Decis Mak. 2018 Jun 14;18(1):38. 
    6. Coloma PM, Becker B, Sturkenboom MC, van Mulligen EM, Kors JA. Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies. Drug Saf. 2015 Oct;38(10):921-30. 

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