By Turacoz Healthcare Solutions | World Liver Day 2025

In a world where social media dominates wellness discussions, liver health is a popular topic — albeit not always a well-informed one. From miracle cures to detox diets, the liver is the focus of myriad health claims with little scientific basis. On this World Liver Day, we highlight the vital role of health journalism in separating fact from fiction and delivering accurate, science-based information.

Emergence of Misinformation about Liver Health

Digital media have opened health information to everyone—but with such openness comes an influx of unfiltered information. Wellness bloggers, often with little to no medical training, tout liver ‘cleanses’ and ‘superfoods’ without accountability. These messages, though alluring, can mislead consumers, undermine patient education campaigns, and jeopardize liver health.

False information not only causes confusion but also can delay treatment and accurate diagnosis. With early treatment being essential in conditions such as non-alcoholic fatty liver disease (NAFLD), hepatitis, and cirrhosis, patients are at a loss if discussion centers around misinformation.

The Role of Health Journalism in Public Health

Evidence-based health journalism acts as a link between health practitioners and the general population. Accurate, accessible, and understandable health reporting translates complex medical jargon into clear messages, enhancing health literacy and supporting informed decision-making.

Medical writers and journalists have an obligation to:

• Confirm information from evidence-based sources like peer-reviewed journals and clinical practice guidelines.
• Work with specialists such as hepatologists, nutritionists, and scientists.
• Employ health information management systems to track, interpret, and share accurate data.

Fact-checking in the Social Media era

With misinformation traveling at lightning speed on the internet, fact-checking now forms a bedrock of trustworthy health communication. Using health information technology like automated content verification systems and AI-based surveillance, medical communicators can quickly address misleading claims and disseminate accurate content to the masses.

Agencies, along with journalists, should also predict patterns. Tracking social media discussions enables them to correct misleading reports in a timely fashion and present scientifically correct counter-information.

Empowering Patients Through Education

Correct health information not only educates, it empowers. Liver health education should be centered in useful, actionable information: comprehending liver function, identifying risks, adopting evidence-based dietary practices, and recognizing warning signs that necessitate medical care.

By ensuring that educational resources are synchronized with national health goals and governmental standards, medical communicators contribute effectively to national campaigns promoting liver health.

A Partnership with Health Professionals

Effective health journalism does not exist in a vacuum. It is developed in close collaboration with health professionals, patient advocacy organizations, and public health organizations. Such collaborations help guarantee that information is up to date, applicable, and consistent with clinical best practices.

The way forward

Combating misinformation on matters related to the liver necessitates a multifaceted approach:

In Conclusion

Being a trusted medical communications agency, Turacoz is of the opinion that science-based, accessible, and honest communication is central to improved health outcomes. On this World Liver Day, we reaffirm our values of truth, science, and service—because timely information is a matter of saving people’s lives.

The healthcare industry is experiencing a paradigm shift as patient voices take center stage in drug development and approval processes. While clinical trial data continues to be the cornerstone of drug development and approval, patient-reported outcomes (PROs) are emerging as the gold standard for demonstrating real-world value, particularly when it comes to market access approvals offering valuable insights into the patient experience and treatment impact. As healthcare systems worldwide shift toward value-based care, PROs are getting more and more widely used in clinical trials and approval processes. For example, the proportion of industry-sponsored oncology trials including PROs assessments rose from 26% (2007–2013) to 75% (2014–2018).¹ This increased integration of PROs into clinical trials and regulatory submissions reflects a growing acknowledgment of their value in evaluating therapies from the patient’s perspective.

The Evolution of Healthcare Metrics

Historically, drug approvals focused primarily on “hard” clinical endpoints, while these metrics remain important, they tell only part of the story, and do not completely capture the picture of quality of life (QoL) or daily functioning. PROs that reflect the patient’s direct perspective on their symptoms, functional status, and overall well-being capture this crucial dimension that clinical data alone cannot measure complementing the traditional clinical outcomes. This holistic view of treatment effects is particularly important for chronic and debilitating conditions, where symptom burden and QoL are critical determinants of treatment success.^2,3

Regulatory Recognition

Regulatory bodies worldwide have recognized this gap and are increasingly demanding PRO data as part of approval submissions:

• The U.S. Food and Drug Administration (FDA)¹ has established Patient-Focused Drug Development (PFDD) to encourages patient participation in R&D decision-making process with an aim to develop a drug which better meets the patients’ needs. PROs were included in FDA’s 53% of medical device authorizations.⁴

• The EMA’s regulatory guidance explicitly recommends PROs inclusion for many therapeutic areas. For example, 78.1% of oncology approvals by EMA included PROs.⁵
• Health technology assessment (HTA) bodies and reimbursement agencies are leveraging PROs to evaluate the value of treatments in real-world settings. This trend is supported by studies showing that PROs can identify low-value care and inform cost-effectiveness analyses, thereby optimizing resource allocation in healthcare systems.⁶ 

The increasing integration of PROs into regulatory submissions underlines their significance in demonstrating treatment benefits from a patient perspective.

The Market Access Imperative

For pharmaceutical companies PRO data is becoming essential for market access success. Here’s why:

Differentiation in Crowded Markets

PRO data can help to distinguish therapies, especially in oncology post-progression scenarios. A study has indicated that positive PRO data such as superior symptom relief, improved physical functioning support continued therapy at the physician’s discretion upon regulatory approval, even in progressive disease.⁷

Pricing and Reimbursement Leverage

The U.S. healthcare system is shifting from fee-for-service to value-based payment models to enhance patient care quality and control costs. Under the 2015 Medicare Access and Children’s Health Insurance Program Reauthorization Act, providers will be assessed on quality and cost efficiency, affecting their reimbursement rates. PROs play a key role in this transition by offering insights into patient preferences, experiences, and perceptions of benefits and risks. These insights inform pricing, reimbursement, and benefit-risk assessments, ensuring treatments align with patient values. PROs also influence health technology assessments by evaluating the impact of medical technologies on quality of life, guiding more equitable pricing decisions based on what patients value.^8,9

Formulary Placement and Treatment Guidelines

Clinical practice guidelines are giving greater weight to PROs evidence when making recommendations. For example, European Society for Medical Oncology (ESMO) recommends symptom monitoring using patient-reported outcome measures (PROMs) for patients with stage IIIB/IV lung cancer who have completed initial or maintenance treatment. Additionally, it also recommends PROMs in survivorship care of patients after treatment of cancer, to improve communication and identify late toxicities, symptoms or functional impairment warranting supportive care.¹⁰

Beyond regulatory initiatives, incorporating PROs can increase the “value” of your therapeutic from a payor perspective, ultimately helping formulary placement.¹¹

Advances in Digital Data Collection Have Made Collecting PROs Easy

The advent of digital health technologies has facilitated the collection and analysis of PROs, making them more accessible and actionable. Electronic patient-reported outcome measures (ePROMs) enable real-time data capture, reducing barriers to implementation and improving the quality of PRO data.^6,12  For example, digital platforms are being used to collect PROs in large-scale studies, such as the PROMchronic study in Germany, which aims to evaluate the effectiveness of ePROMs in improving care for patients with chronic diseases like diabetes and asthma.⁶ Additionally, AI and machine learning help analyze PRO data to identify patterns and insights.

Challenges and Opportunities

Despite their growing importance, the use of PROs in market access approvals is not without challenges. Issues such as the lack of standardization, variability in data quality, and the need for robust methodologies remain. However, ongoing research and policy initiatives are addressing these challenges, with a focus on developing validated instruments, improving data collection practices, and integrating PROs into regulatory frameworks.^13,14

For example, the European Medicines Agency (EMA) has emphasized the need for harmonization of PRO measures to facilitate their use in drug development and regulatory decision-making. Similarly, initiatives like the Innovative Medicines Initiative (IMI) PREFER project are working to establish best practices for incorporating patient preferences into regulatory evaluations.^14,15

The Future of PROs in Market Access

The future of PROs in market access approvals is promising, with ongoing advancements in technology, policy, and methodology. As regulators and payers increasingly recognize the value of patient-centered data, PROs are likely to become even more integral to healthcare decision-making. Their ability to capture the patient’s perspective, complement traditional outcomes, and support real-world evidence makes them indispensable in the era of value-based healthcare.¹⁶

Conclusion

In conclusion, PROs are becoming the gold standard for market access approvals because they provide a patient-centered perspective, complement traditional clinical outcomes, and support regulatory and reimbursement decisions with real-world evidence. As healthcare systems continue to evolve, the integration of PROs into decision-making processes will remain a cornerstone of value-based, patient-centered care.

References

1. Cao K, et al. From the Formation of Conceptual Framework to Regulatory Decision-Making: Considerations for the Developments of Patient-Reported Outcome Instruments. Drug Des Devel Ther. 2024;18:5759-5771.
2. Bonsel JM, et al. The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review. Health Qual Life Outcomes. 2024;22(1):101.
3. Jeyaraman N, et al. Voices that matter: The impact of patient-reported outcome measures on clinical decision-making. World J Methodol.2025; 15(2):98066.
4. Matts ST, et al. Inclusion of patient-reported outcome instruments in US FDA medical device marketing authorizations. J Patient Rep Outcomes. 2022;6(1):38.
5. Teixeira MM, et al. A review of patient-reported outcomes used for regulatory approval of oncology medicinal products in the European Union between 2017 and 2020. Front Med (Lausanne). 2022;9:968272.
6. Nikkhah J, et al. Evaluating the Population-Based Usage and Benefit of Digitally Collected Patient-Reported Outcomes and Experiences in Patients With Chronic Diseases: The PROMchronic Study Protocol. JMIR Res Protoc. 2024;13:e56487.
7. Brogan AP, et al. Payer Perspectives on Patient-Reported Outcomes in Health Care Decision Making: Oncology Examples. J Manag Care Spec Pharm. 2017;23(2):125-134.
8. Chachoua L, et al. Use of Patient Preference Information in Benefit-Risk Assessment, Health Technology Assessment, and Pricing and Reimbursement Decisions: A Systematic Literature Review of Attempts and Initiatives. Front Med (Lausanne). 2020;7:543046.
9. Squitieri L, Bozic KJ, and Pusic AL. The Role of Patient-Reported Outcome Measures in Value-Based Payment Reform. Value Health. 2017;20(6):834-836.
10. Di Maio M, et al. The role of patient-reported outcome measures in the continuum of cancer clinical care: ESMO Clinical Practice Guideline. Ann Oncol. 2022;33(9):878-892.
11. Oderda G, et al. Payer perceptions on the use of patient-reported outcomes in oncology decision making. J Manag Care Spec Pharm. 2022;28(2):188-195.
12. Joeris A, et al. Real-world patient data: Can they support decision making and patient engagement?. Injury. 2023;54 Suppl 3:S51-S56.
13. Almeida D, et al. Leveraging patient experience data to guide medicines development, regulation, access decisions and clinical care in the EU. Front Med (Lausanne). 2024;11:1408636.
14. Janssens R, et al. How can patient preferences be used and communicated in the regulatory evaluation of medicinal products? Findings and recommendations from IMI PREFER and call to action. Front Pharmacol. 2023;14:1192770.
15. Ciani O, et al. Patient-reported outcome measures in drugs for neurological conditions approved by European Medicines Agency 2017-2022. Neurol Sci. 2023;44(8):2933-2937.
16. Adeghe EP, Okolo CA, and Ojeyinka OT. The influence of patient-reported outcome measures on healthcare delivery: A review of methodologies and applications. OARJBP. 2024;10(2):013-21.

In the evolving era of healthcare, data is the foundation of informed decision-making. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), real-world evidence (RWE) generation is undergoing a revolutionary transformation. AI-driven analytics empower researchers and healthcare professionals (HCPs) to extract meaningful insights from vast and complex datasets which ultimately improve patient outcomes and optimize treatment strategies.

The power of RWE in healthcare

AI and ML are playing a pivotal role in bridging the gap between controlled clinical trials and real-world clinical practices by enabling seamless synthesis and interpretation of diverse datasets. These technologies help in aligning clinical evidence with real-world treatment patterns and outcomes, making the data more applicable and impactful for regulatory documentation. Through automated data extraction, natural language processing, and real-time analytics, AI supports the creation of timely and compliant regulatory submissions that reflect real-world treatment efficacy and safety. In publication planning, ML can identify emerging data trends and prioritize high-impact topics, while AI-driven tools streamline manuscript generation and literature analysis. Additionally, in Health Economics and Outcomes Research (HEOR), AI enhances model precision by incorporating dynamic, real-world variables—leading to more robust cost-effectiveness and budget impact assessments that resonate with payers and policymakers.

How AI & ML transform RWE generation

1. Data integration & processing

Healthcare data is often fragmented across multiple systems, making integration a major challenge. AI-driven algorithms efficiently harmonize disparate datasets, standardizing information from diverse sources such as:

• EHRs: AI extracts relevant clinical information while maintaining patient privacy.
• Wearable & sensor data: Continuous monitoring devices provide real-time insights into patient health trends.
• Medical imaging & genomic data: AI enhances pattern recognition, enabling precision medicine approaches.

2. Predictive analytics for better decision-making

ML models analyze historical patient data to predict outcomes, identify disease progression, and assess treatment efficacy. For example:

• Early disease detection: AI models detect anomalies in imaging scans or lab results, enabling early intervention.
• Treatment optimization: By analyzing patient responses to therapies, ML suggests tailored treatment plans, reducing trial-and-error approaches.
• Risk stratification: AI helps classify patients based on risk factors, aiding in proactive disease management.

3. Enhancing clinical trials & drug development

AI and ML streamline clinical research by:

• Patient recruitment: Identifying eligible participants through automated data analysis.
• Synthetic control arms: Using AI-generated patient models to simulate control groups, reducing the need for large trial populations.
• Real-time monitoring: AI continuously tracks patient responses, adjusting protocols dynamically for optimal results.

4. Improving pharmacovigilance & safety monitoring

Post-market drug surveillance benefits from AI’s ability to detect adverse events from vast datasets, including:

• Social media & patient forums: AI scans digital discussions for emerging side effect patterns.
• EHRs & claims data: Identifies unexpected adverse reactions across large patient populations.
• Natural Language Processing (NLP): Extracts insights from unstructured physician notes and reports.

Real-world impact of AI & ML in RWE generation

AI-driven RWE applications are already making tangible improvements in healthcare:

• Personalized medicine: AI enables the development of individualized treatment plans based on genetic, environmental, and lifestyle factors.
• Chronic disease management: ML models predict disease exacerbations, prompting timely interventions.
• Health policy & public health initiatives: AI-driven RWE informs regulatory decisions, optimizing healthcare resource allocation.

Challenges & Solutions in AI-Powered RWE Generation

The future of AI in RWE

As AI and ML continue to advance, their role in RWE generation will expand, fostering

• More efficient drug approvals: Regulatory bodies increasingly rely on AI-enhanced RWE to accelerate decision-making.
• Improved patient-centric care: AI-powered insights enable more holistic, tailored treatment plans.
• Greater integration with wearable tech: Continuous patient monitoring enhances real-time evidence collection.

Turacoz remain committed to scientific integrity, clear communication, and regulatory compliance. Our AI-enhanced approach to RWE documentation ensures that valuable real-world insights are effectively translated into actionable information for all stakeholders.

By combining medical writing expertise with advanced AI and ML capabilities, we help our clients transform complex real-world data into compelling evidence narratives that advance medical knowledge, support regulatory decisions, and ultimately improve patient care.

In an era where healthcare decisions are increasingly driven by data, real-world evidence (RWE) has become a crucial tool for assessing treatment effectiveness beyond controlled medical trials. Real-world data (RWD) provides insights into how medical interventions perform across diverse patient populations in routine practice. However, concerns regarding bias, data integrity, and regulatory compliance raise an important question: How reliable are real-world studies?

The Growing Importance of RWE

Unlike traditional clinical trials, which follow strict protocols and eligibility criteria, real-world studies rely on data from electronic health records (EHRs), insurance claims, patient registries, and even wearable devices. This shift allows researchers, policymakers, and healthcare professionals to evaluate the long-term safety, cost-effectiveness, and impact of treatments in real-world settings.

Medical affairs teams use RWE to support health economics research, inform market access strategies, and guide regulatory decision-making. However, ensuring the credibility of findings requires a proactive approach to addressing biases and enhancing data quality.

Common Biases in Real-world Studies

Real-world studies are vulnerable to multiple forms of bias, which can compromise their reliability:

• Selection bias: Since real-world studies do not employ randomized patient selection, certain demographic or clinical groups may be overrepresented or underrepresented, leading to skewed results.
• Confounding variables: Unlike randomized controlled trials (RCTs), real-world studies often lack mechanisms to isolate variables, making it difficult to establish causality.
• Reporting bias: Incomplete or inconsistent data entry in electronic health records and insurance claims databases can introduce errors that affect study conclusions.
• Publication bias: Studies with favorable outcomes are more likely to be published, creating an incomplete picture of a treatment’s true effectiveness.

Mitigating Bias in RWE

Several methodologies can help mitigate bias in RWE studies:

1. Propensity score matching (PSM): This statistical technique matches patients with similar baseline characteristics to reduce confounding.
2. Inverse probability weighting (IPW): A weighting method that adjusts for imbalances in patient characteristics, improving comparability.
3. Sensitivity analyses: Conducting multiple analyses with different assumptions helps assess the robustness of findings.
4. Use of linked datasets: Combining multiple data sources (e.g., EHRs, registries, and claims data) can improve data completeness and reduce missingness-related biases¹.

Ensuring Data Quality in Real-world Studies

Improving the reliability of RWE requires stringent methodologies and advanced analytical tools. Strategies to enhance data quality include:

• Systematic literature reviews: Conducting thorough literature reviews ensures that RWE studies incorporate all relevant data, reducing the risk of biased conclusions².
• Artificial intelligence in healthcare: AI-driven analytics can identify patterns, clean datasets, and account for missing variables, leading to more reliable insights³.
• Standardized data collection: Implementing structured reporting systems across healthcare institutions ensures greater consistency and completeness in real-world data⁴.
• Regulatory compliance: Adhering to guidelines set by regulatory bodies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) ensures that real-world studies meet rigorous scientific and ethical standards⁵.

The Role of Regulatory Compliance in RWE Reliability

To incorporate RWE into clinical decision-making, regulatory bodies have introduced stringent data governance frameworks. Ensuring compliance with Good Clinical Practice (GCP) and other regulations mitigates the risks associated with incomplete or biased data.

• The FDA’s Real-World Evidence Framework establishes standards for assessing RWD quality, study design, and applicability in regulatory submissions⁶.
• The EMA emphasizes transparency and reproducibility in RWE submissions, ensuring that studies meet the highest scientific standards⁷.

For example, the FDA approved Palbociclib (Ibrance) for male breast cancer based on RWE from claims and EHR data rather than traditional clinical trials⁸. This case highlights how high-quality RWE can inform regulatory decisions when RCTs are impractical.

Future Outlook: Combining RWE with Clinical Trials

While RCTs remain the gold standard for evaluating treatment efficacy, RWE plays a complementary role by providing insights into long-term safety, patient adherence, and economic impact. Integrating real-world data with traditional research methodologies can create a more comprehensive understanding of healthcare interventions.

Advancements in AI-driven analytics, real-time data integration, and digital health monitoring are improving the accuracy of RWE studies. Organizations are increasingly leveraging these technologies to refine data accuracy and eliminate bias⁹. By embracing the best practices in systematic literature review, regulatory compliance, and data validation, real-world studies can offer valuable insights that drive evidence-based healthcare decisions.

The Path Forward

RWE is a powerful tool in modern healthcare, but its reliability depends on addressing biases and ensuring data integrity. Implementing standardized methodologies, leveraging artificial intelligence, and adhering to regulatory standards can help unlock the full potential of real-world studies and effectively disseminate findings across the healthcare ecosystem.

References

1. Schneeweiss S. Learning from big health care data. N Engl J Med. 2014;370(23):2161-3.
2. Wang SV, Pinheiro S, Hua W, et al. STaRT-RWE: structured template for planning and reporting on the implementation of real-world evidence studies. BMJ 2021;372:m4856.
3. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58.
4. FDA. Real-world evidence: what is it and what can it tell us? [Internet]. 2023 [cited Feb 27, 2025]. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
5. European Medicines Agency. Real-world evidence in regulatory decision-making [Internet]. 2022 [cited Feb 27, 2025]. Available from: https://www.ema.europa.eu/en/human-regulatory/post-authorisation/real-world-evidence
6. US FDA. Framework for FDA’s real-world evidence program [Internet]. 2018 [cited Feb 27, 2025]. Available from: https://www.fda.gov/media/120060/download
7. European Medicines Agency. Guideline on good pharmacovigilance practices (GVP) [Internet]. 2021 [cited Feb 27, 2025]. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices_en.pdf
8. US FDA. FDA approves Ibrance for male breast cancer based on real-world evidence [Internet]. 2019 [cited Feb 27, 2025]. Available from: https://www.fda.gov/news-events/press-announcements/fda-approves-ibrance-male-breast-cancer-based-real-world-evidence
9. Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and regulatory decision making: where are we now? Clin Pharmacol Ther. 2018;104(5):822-9.

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