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Empowering Patient Engagement in HTA: Lessons from an AI-Generated Plain Language Summary Case Study

The challenge: Making HTA understandable to everyone

Health technology assessments (HTAs) play a critical role in determining which treatments and innovations are adopted within healthcare systems. However, the technical language and complexity of HTA reports often make them inaccessible to patients and caregivers — the very individuals whose lives these decisions affect the most.

Plain Language Summaries (PLS) are designed to close this gap. They can translate HTA findings into clear, patient-friendly language, empowering people to engage meaningfully in healthcare decisions. Yet, producing high-quality PLS documents is a slow and resource-intensive process. Teams must balance scientific rigor with readability, cultural sensitivity, and accuracy — a demanding task that limits scalability.

This is where artificial intelligence (AI) offers a transformative opportunity.

The study: Can generative AI help bridge the communication gap?

At ISPOR Europe 2025, we presented a pioneering study exploring whether generative AI can create accurate and patient-friendly summaries from complex HTA documents.

Using a NICE Highly Specialized Technologies (HST) guidance on onasemnogene abeparvovec (a gene therapy for spinal muscular atrophy), the team tested Google Gemini, a large language model, to generate a full PLS automatically.

The AI-generated summary was evaluated across 18 quality measures covering readability, accuracy, relevance, and tone. A “human-in-the-loop” reviewer ensured alignment with patient communication standards and European HTA Regulation principles — integrating transparency and patient empowerment into the assessment.

The results: Speed meets substance

The results were striking. The AI produced an eight-page (2,570-word) PLS in just 15 seconds, structured around all key HTA components — disease context, treatment mechanism, clinical effectiveness, safety, and patient impact.

Across 18 evaluation criteria, the PLS achieved an average score of 8.27/10, reflecting strong alignment with plain language and patient-centered communication standards.

Mechanism simplicity (9.2/10) and plain language explanation (8.9/10) were top-performing categories, demonstrating Gemini’s ability to simplify complex gene therapy concepts without sacrificing accuracy.
The document met CEFR B1 readability, ensuring accessibility for non-specialist audiences.

However, the AI struggled with target population clarity (6.8/10) and unmet need articulation (6.5/10) — areas requiring deeper contextual and emotional nuance. These findings underscore the importance of maintaining a human role in refining and validating AI outputs, especially when tailoring content for specific patient groups.

The implications: Toward patient-centered HTA with AI

The study demonstrates that AI can accelerate and enhance the creation of patient-friendly HTA communications, promoting inclusivity and transparency in healthcare decision-making. But it also emphasizes that AI should complement, not replace, human expertise.

Generative AI tools like Gemini can help:

Scale patient engagement, enabling broader and faster dissemination of accessible HTA information.
Support regulatory compliance, aligning with EU HTA Regulation principles of transparency and participation.
Enhance health literacy, fostering more equitable and informed patient involvement.

Yet, meaningful adoption requires:

Human-in-the-loop systems to verify accuracy, tone, and contextual relevance.
Prompt optimization to capture nuances like unmet needs or cultural differences.
Ongoing validation to ensure reliability and regulatory alignment.

The conclusion: AI as a partner in patient empowerment

This work highlights how AI, when thoughtfully integrated, can make HTA more human-centered, transparent, and inclusive. Rather than automating empathy, it can help scale understanding — bringing patients into the conversation, not leaving them behind.

As HTA continues to evolve under new European regulations, embedding AI into communication workflows may mark a key step toward a truly patient-centered future — where every individual can understand, question, and contribute to the health decisions that shape their lives.

Interested in learning more?

Read the abstract published at ISPOR EUROPE 2025: “Can Generative AI Deliver Patient-Friendly Summaries? A Case Study Using NICE Guidance for Spinal Muscular Atrophy” by Manuel Cossio and Ramiro E. Gilardino.

Consultancy-Curated Global SLRs: A New Precedent for HTA Submissions

Health Technology Assessment (HTA) typically involves the development of systematic literature reviews (SLR) to form the evidence base from which reimbursement decisions are made. Data identified by SLRs are in the public domain and in more common disease areas (such as oncology), sponsors tend to include the same trials and data in their SLRs as their competitors.

Here, we explore 1) how the need for comprehensive global SLRs results in considerable overlap between sponsors’ SLR packages; 2) the increasing challenge of managing large SLRs due to the volume of literature and rate at which new data are published; and 3) a solution to these challenges via curated ready-to-use global SLRs delivered via a subscription model.

How the need for comprehensive SLRs leads to overlap in evidence packages

SLRs for HTA purposes must comprehensively include all treatments for a disease. Narrower approaches specific to a sponsor’s treatment risk introducing selection bias and may be deemed as “cherry-picking” by HTA bodies. As the evidence base evolves, narrow approaches may omit pipeline treatments that go on to become the standard of care, thus limiting the long-term validity of the SLR. In addition, the standard of care differs by country and treatment pathway. Consequently, sponsors must include all treatments relevant to the context of each of their local affiliates, known as a “global SLR.” Sponsors must undergo the same methodological steps to conduct HTA-standard SLRs (search strategies, screening, data extraction), therefore, for a given specific disease, the base-case global SLR will greatly overlap between sponsors in terms of the included trials and data.

The increasing challenge of managing large SLRs

Conducting comprehensive global SLRs that include all available treatments is becoming a greater challenge due to rapidly emerging new treatments and, consequently, the exponentially increasing volume of newly published evidence. Ahead of going to market, sponsors must invest extensively in SLR activities and often require the expertise of external consultants. As the rate at which the evidence base continues to grow, SLR packages can rapidly become obsolete unless newly published evidence is identified via regular updates. Solutions are urgently needed to find more efficient ways for our industry to identify and synthesize data via SLRs. We, as an industry, have a responsibility to patients to reduce duplication and the time it takes for new treatments to be assessed.

A solution: Curated ready-to-use global SLRs

While sponsors traditionally commission a consultancy vendor with the expertise required to conduct their global SLRs de novo, Cytel is leading a shift towards consultancy-curated global SLRs that sponsors can subscribe to. Cytel conducts and updates SLRs in multiple indications delivered through the interactive web-based platform, LiveSLR®, which clients can subscribe to and access regularly updated SLRs. LiveSLR® subscribers can also commission bespoke adaptations and updates to meet their exact requirements. Common requests for bespoke adaptations include extraction of specific subgroup data, inclusion of newly published data ahead of a local submission, and expansion of an indication. Subscribers can also choose Artificial Intelligence (AI) monitoring of new literature so that SLR updates can be optimally timed.

Setting a new precedent for HTA

As Cytel goes to market with curated ready-to-use SLR libraries, we are engaging with members of HTA external assessment groups (EAG). HTA bodies rely on EAGs to critique company submissions in terms of the quality of evidence and methods. Their opinion regarding the acceptability of consultancy-curated SLRs and evidence identification through subscribed SLRs is paramount. To date, this engagement has been very positive, with recognition that SLRs conducted via a third party could reduce both the risk of bias within SLRs and research wastage across the industry.

For EAGs, it is the quality of the submitted SLR itself that is of importance. So long as methods are fully transparent and robust, consultancy-curated SLRs delivered via a subscription do not violate the evidence requirements of HTA bodies. All Cytel SLRs come with full methodology. The LiveSLR® platform is a comprehensive repository that includes full search strategy, Preferred Reporting Items for Systematic reviews, and Meta-Analyses (PRISMA) diagrams, excluded study listing, validated data extractions, and downloadable full reports.

Most health economics and outcomes research/market access professionals rely on precedent to inform their strategy for reimbursement. As the HTA landscape evolves with the incoming European Union Joint Clinical Assessment and other collaborations to reduce duplication, Cytel anticipates that alternative solutions to traditional SLRs will emerge and become a new precedent within the HTA setting.

Cytel has seven regularly updated HTA-compliant SLRs:

Interested in learning more? Watch Vicki’s on-demand webinar “Accelerated HTA Timelines: Unlock the Power of Ready-to-Use SLRs”:

Maximizing the Potential of Real-World Data with Bayesian Borrowing

In response to concerns about data quality in real-world evidence (RWE) generation, including issues such as bias and small sample sizes, resulting in low precision estimates with questionable accuracy and thus interpretability challenges, regulatory submissions have increasingly incorporated advanced methodologies to enhance the robustness of RWE.

Among these methods, Bayesian borrowing stands out as an approach that can significantly increase the scientific potential of real-world data. By leveraging data from multiple sources that may all have different weaknesses, Bayesian borrowing can combine these and enhance the power of comparisons with trial data for comparisons beyond those from a randomized control trial. Bayesian borrowing can also be used to create hybrid control arms, enabling a smaller control cohort to address ethical concerns and patient availability issues.1

The Bayesian borrowing concept

Bayesian borrowing methods make use of external data, potentially from multiple sources, by using a prior distribution that adjusts for the possibility that this external data may come from a different population. While using external or historical data can enhance the precision and accuracy of parameter estimates in a study, directly simple pooling of this data could lead to bias if the external population differs from the current one.2,3,4 To address this, priors such as a power prior is used to adjust the influence of the external data, which is more diffuse than complete pooling of current study dataset and the external dataset, reducing the possible bias but also the eventual precision of the parameter estimate.

In drug development, Bayesian borrowing is primarily applied in situations involving rare diseases, pediatric trials, or when there are no existing approved treatments for the same conditions.5

Figure 1. Bayesian borrowing

Quantitative bias analysis (QBA) plays a crucial role in supporting studies that employ Bayesian borrowing by assessing the impact that the weaknesses in the data being integrated has on study results. When leveraging external or historical data through Bayesian methods, such as Bayesian borrowing, there is always a risk that the borrowed data may introduce bias due to elements that cannot be addressed directly in analysis specifications, such as missing or unmeasured data, or other quality issues. QBA helps to quantify the extent of these biases and provides a structured approach to adjust for them, thereby enhancing the interpretation possibilities of the results, ultimately supporting study validity and scientific integrity.

By applying QBA alongside Bayesian borrowing, researchers can transparently account for uncertainties in the borrowed data and ensure that the final estimates are more robust, credible, and defensible in both regulatory and clinical decision-making contexts.

Figure 2. Example of QBA for Bayesian borrowing

FDA and HTA submissions incorporated with Bayesian borrowing methods

In recent years, the acceptance of Bayesian borrowing approaches has been evolving from both regulatory and Health Technology Assessment (HTA) perspectives.

The FDA has highlighted this shift through initiatives like a podcast discussing the use of Bayesian statistics, including a case where Bayesian methods were used to borrow data from an adult trial to assess an asthma product’s treatment effects in pediatric patients.6 Additionally, the FDA recommended that GSK apply Bayesian dynamic borrowing to integrate adult trial data for a pediatric study for post-marketing activities, and these results were subsequently accepted.7

HTA bodies are also considering Bayesian methods; for example, NICE recommended using Bayesian hierarchical models, which are closely related to Bayesian borrowing, in the technical appraisal of larotrectinib for NTRK-fusion positive solid tumors in 2020.8

Furthermore, the FDA plans to release draft guidance on the use of Bayesian methods in clinical trials for drugs and biologics by the end of 2025.

The future of Bayesian borrowing

Although Bayesian methods have garnered increasing attention from regulatory and HTA bodies, their practical implementation has been somewhat limited. Challenges such as organizational resistance to novel approaches, resource constraints, and difficulties in applying these advanced methods effectively can hinder their adoption in regulatory and HTA submissions. However, as awareness grows and best practices are established, these barriers are likely to diminish, paving the way for more widespread use of Bayesian methods.

Notes

1 Dron, L., Golchi, S., Hsu, G., & Thorlund, K. (2019). Minimizing Control Group Allocation in Randomized Trials Using Dynamic Borrowing of External Control Data – An Application to Second Line Therapy for Non-Small Cell Lung Cancer. Contemporary Clinical Trials Communications, 16(1).

2 Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S., Roychoudhury, S., & Thompson, L. (2013). Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials. Pharmaceutical Statistics, 13(1).

3 Struebing, A., McKibbon, C., Ruan, H., Mackay, E., Dennis, N., Velummailum, R., He, P., Tanaka, Y., Xiong, Y., Springford, A., & Rosenlund, M. (2024). Augmenting External Control Arms Using Bayesian Borrowing: A Case Study in First-Line Non-Small Cell Lung Cancer. Journal of Comparative Effectiveness Research, 13(5).

4 Mackay, E. K. & Springford, A. (2023). Evaluating Treatments in Rare Indications Warrants a Bayesian Approach. Frontiers in Pharmacology, 14(1).

5 Muehlemann, N., Zhou, T., Mukherjee, R., Hossain, M. I., Roychoudhury, S., & Russek‑Cohen, E. (2023). A Tutorial on Modern Bayesian Methods in Clinical Trials. Therapeutic Innovation & Regulatory Science, 57(1).

6 Clark, J. (2023). Using Bayesian Statistical Approaches to Advance our Ability to Evaluate Drug Products. CDER Small Business and Industry Assistance Chronicles, U.S. FDA.

7 Best, N., Price, R. G., Pouliquen, I. J., & Keene, O. N. (2021). Assessing Efficacy in Important Subgroups in Confirmatory Trials: An Example Using Bayesian Dynamic Borrowing. Pharmaceutical Statistics, 20(1).

8 NICE. (2020). Appraisal Consultation Document: Larotrectinib for Treating NTRK Fusion-Positive Solid Tumours.

Simulating Survival Outcomes for Unanchored Simulated Treatment Comparisons: Guidance on Censoring Approaches

Unanchored simulated treatment comparisons (STCs) are a valuable tool for manufacturers navigating the health technology assessment (HTA) landscape. When head-to-head clinical trials are unavailable, STCs allow for population-adjusted indirect comparisons between a single-arm trial and an external control arm.

Using regression modeling to predict outcomes based on patient characteristics, STCs enable comparisons in the absence of a common comparator. This is particularly valuable when evaluating novel therapies, especially in rare or specialized disease areas where randomized controlled trials may be limited.

External Validity Bias in HTA Submissions: A Case for Transportability Methods

Health technology assessment (HTA) bodies support decision-making for the reimbursement of new technologies at the local or national level. Recommendations made by HTA bodies are based on various sources of evidence, ranging from the preferred standard randomized clinical trials to real-world data (RWD) when trials are unavailable or not relevant to the target population of the decision problem. Non-randomized studies of treatment effects are already widely used in rare diseases and innovative technologies to contextualize findings from single-arm trials. Watch our recent webinar on real-world external control arms here.

To build trust in the evidence that supports decision making, researchers need to understand and address potential risks to study validity.

Planning Strategies for Externally Controlled Trials: Insights from ISPOR US 2024

External Control Arms (ECAs) provide comparative evidence when recruiting patients is difficult or unethical in randomized controlled trials. ECAs have significant potential to save resources and accelerate access to innovative treatment. In a previous blog, our experts took a deep dive into the concept of ECAs, their acceptable use cases, and the current regulatory guidance.

Existing guidance on the design and conduct of externally controlled trials emphasizes the importance of early engagement with regulatory and HTA bodies to justify using an ECA and discuss the preliminary study design and statistical analyses. With the increasing use of ECAs in regulatory and HTA submissions, the acceptable use cases for ECAs and important design and analytical considerations are becoming clearer. However, sponsors still face key questions about the optimal timing to plan for an ECA and how to prepare for early interactions to address differing regulatory and HTA perspectives.

In the ISPOR US 2024 HEOR Theatre session, Jason Simeone, Evie Merinopoulou, and Grace Hsu delved into these questions, discussing how regulatory and HTA stakeholders appraise ECAs, common issues from both perspectives and proposed practical solutions. In this blog, we ask Evie follow-up questions, highlighting insights from their ISPOR HEOR Theater session.

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Ideally, sponsors that need to perform an ECA to support their development program should start planning for the ECA alongside the clinical trial design. This allows them to gain experience with current real-world data (RWD) and make any necessary investment decisions for data improvements, such as additional data collection or infrastructure upgrades. Further, considering an ECA during the trial design provides the opportunity to incorporate real-world endpoints into the clinical trial. This is particularly valuable because defining clinical endpoints in real-world databases can often be challenging, especially when they are not measured consistently between routine practice and clinical trials.

Further, we showed in our presentation how although formal guidance from regulatory and HTA bodies on ECAs is consistent, final decisions when appraising ECAs may differ. This divergence in regulatory vs HTA acceptance reflects differing requirements for ECAs. When planning ECAs, both perspectives and requirements should be considered. Therefore, within sponsor organizations, early planning is key for cross-functional alignment (between HEOR/Market Access and Medical Affairs teams) on ECA study objectives and design, leading to more efficient evidence planning. With regards to external engagements with regulators and payers, the optimal timing is very contextual, but generally, sponsors should engage with decision makers via available routes like early advice programs, early enough to have the time to incorporate feedback and adjust their RWD strategy and study design—before protocol and SAP finalization.

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Yes, absolutely. During the early feasibility assessments that we discussed in our ISPOR presentation, we should evaluate a range of elements to determine the feasibility of an ECA—ranging from the identification of target populations to the reliable capture of confounders and study endpoints, among other factors. Identifying relevant comparators is only one element of those assessments. Even if comparators change over time, becoming familiar with RWD and current gaps helps inform discussions about the appropriate data strategy and design, which should be flexible enough to reflect some of the changes in the treatment landscape. Perhaps now, we would want to know if treatments are well captured and elements like patient count on a relevant comparator will need to be refreshed. It is important to ask questions during the early planning stages that are specific yet broad enough to inform ECA feasibility, even if the research question evolves, particularly concerning the RWD strategy.

You’ve recommended that study sponsors should be prepared to discuss certain topics during early engagement meetings, such as the ECA rationale, data source, early design considerations, and feasibility assessment. In a resource-constrained environment, sponsors may not want to invest so much money in these activities before the very first meeting, only to receive a negative response. What topics should be prioritized for that first engagement with an HTA or regulatory agency vs. subsequent meetings?

This is an important point. Ultimately, the most crucial aspect is to clarify the justification for an ECA and assess whether the agencies are open to considering evidence from an ECA. Working with the right experts who understand agency requirements from prior experience is important. Beyond the justification for an ECA being clear, we see that most critiques of ECAs stem from data issues. So, in my opinion, presenting external data source options and discussing anticipated challenges can facilitate a more productive discussion in those early engagements. If resources are constrained, a more targeted review is sufficient rather than a full-blown data landscaping exercise.

During your presentation, you emphasized the importance of identifying fit-for-purpose data. However, in some cases, a sponsor may have to submit to an HTA body in a region where such data is not readily available. For instance, if a detailed data landscaping assessment reveals that most fit-for-purpose data is in the US, but the submission is for a European HTA agency, how can sponsors address this challenge in their submission?

First and foremost, sponsors need to present to the local agency that they have thoroughly attempted to identify a data source accurately representing the local (target) population of interest. Local agencies are usually quite understanding if sponsors can demonstrate that they made the necessary effort and did not cherry-pick data sources, but instead selected a source with the highest quality data available for the research question, in a transparent and systematic manner. However, this means that there might be some potential external validity bias that could concern a local decision-maker. For example, a UK or German payer might be concerned that evidence submitted from a US data-derived ECA may not be generalizable to the target population of the decision problem.

At Cytel, we have been engaged in some very interesting work to understand how we could adjust for this potential external validity bias using transportability methods. These are quantitative methods, similar to those adjusting for confounding, and can be reliably used to extend conclusions from one study population to an external target population. Essentially, if core evidence comes from a US-data derived ECA, transportability methods can be applied to adjust the study findings to measurable patient characteristics in the target population of interest, accounting for prognostic factors or effect modifiers. We recently published a demonstration project on this topic [1 ]. Additionally, NICE recently updated its RWE framework [2] to include transportability analysis methods.

Alternatively, sponsors could consider designing a prospective study, though this approach requires much higher costs and extensive timelines. If you’re taking this route, you should design data collection with the ECA in mind, aligning patient selection criteria, endpoint definitions, etc., which is why planning early is important.

Overall, at Cytel we encourage sponsors to approach data selection in a transparent and systematic way, as recommended across all existing ECA formal guidance documents, and leverage available analytical approaches to address potential external validity concerns when using non-local data if additional data collection is not feasible.

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?

Typically, in sponsor organizations, there are clinical development and medical affairs teams that understand regulatory requirements and processes very well. In addition, there are Market Access and HEOR/RWE teams that know RWD and real-world evidence methods very well. These teams may not always work closely together, but in our presentation, we talked about the importance of bringing these two teams together early on in planning for ECAs to align differing regulatory vs payer requirements. When selecting external partners, it’s important to work with organizations that have important methodological and technical expertise. They should also have a thorough understanding of the evolving guidance and acceptance criteria of decision-making agencies and be able to provide strategic guidance on important study design decisions and early stakeholder engagements.

Interested in exploring further? Download the slides from the ISPOR HEOR Theatre Session presented by Cytel here.

Notes

[1] Ramagopalan SV, Popat S, Gupta A, et al. Transportability of Overall Survival Estimates From US to Canadian Patients With Advanced Non–Small Cell Lung Cancer With Implications for Regulatory and Health Technology Assessment. JAMA Netw Open. 2022;5(11):e2239874. doi:10.1001/jamanetworkopen.2022.39874

[2] https://www.nice.org.uk/corporate/ecd9/resources/nice-realworld-evidence-framework-pdf-1124020816837

Unravelling PICO: The Pillars of the European Joint Clinical Assessment

The European Union (EU) health technology assessment (HTA) regulation aims to improve the availability of innovative technologies for patients across the European Union (EU).¹ It is also claimed to offer efficiency gains for manufacturers, due to one single EU-level submission vs. multiple parallel submissions to different HTA bodies.² In this blog, we will first introduce the Joint Clinical Assessment (JCA), a legal requirement of the EU HTA regulation; the pillars that hold the JCA together, the PICO framework; and the consequential impact to manufacturers on reporting requirements based on multiple PICO.

The Need for a “Living” Approach to HTAs

The Need for Structured Tools to Guide HTA Submissions

The New EU HTA Landscape: Insights on Indirect Evidence

How should health technology developers prepare for future market access activities in Europe?

Numerous discussions have already taken place in various forms and on various platforms around the upcoming implementation of the EU Joint Clinical Assessments (JCA); it’s a hot topic and keeps many of us in our industry occupied. Despite the European Commission’s active efforts in developing draft regulation and related materials to support the transition to JCA, to date, some questions remain unanswered. As the EU JCA aims to harmonize and accelerate evaluation processes in Europe, all stakeholders, including health technology developers, national health technology assessment (HTA) authorities, and EU JCA assessors and patients, are facing substantial changes with this new process on European-level HTA.

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The challenge: Making HTA understandable to everyone

The study: Can generative AI help bridge the communication gap?

The results: Speed meets substance

The implications: Toward patient-centered HTA with AI

The conclusion: AI as a partner in patient empowerment

Interested in learning more?

How the need for comprehensive SLRs leads to overlap in evidence packages

The increasing challenge of managing large SLRs

A solution: Curated ready-to-use global SLRs

Setting a new precedent for HTA

Cytel has seven regularly updated HTA-compliant SLRs:

Interested in learning more? Watch Vicki’s on-demand webinar “Accelerated HTA Timelines: Unlock the Power of Ready-to-Use SLRs”:

The Bayesian borrowing concept

Figure 1. Bayesian borrowing

Figure 2. Example of QBA for Bayesian borrowing

FDA and HTA submissions incorporated with Bayesian borrowing methods

The future of Bayesian borrowing

Notes

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?