Demand for transcatheter aortic valve implantation (TAVI) has increased in the last decade and has outpaced system capacity, impacting wait times and bringing undesirable health outcomes such as waitlist mortality and number of urgent procedures. Risk-based prioritization can improve equitable access to patients. In this study, we assess the impacts of different classifications and wait times for each risk group on health outcomes.

We developed decision-analytic models that simulate the patient trajectory from referral to completion of TAVI. Using prediction models that can classify patients based on their risk of adverse events on the waitlist, we assessed the impacts of (i) the number of risk groups, (ii) size of the risk groups, and (iii) recommended wait times for each risk group, on waitlist mortality, hospitalization, and the proportion of urgent TAVIs. All scenarios were modeled under the same resource constraints, allowing us to explore the trade-offs between faster access to prioritized patients and deferred access to nonprioritized groups.

Increasing the number of risk groups from two to three, increasing the sizes of the higher-risk groups from five percent to 30 percent of the cohort each, and providing faster access to the higher-risk groups (five to three weeks for high-risk and 11 to five weeks for medium-risk) achieved the greatest reductions in mortality, hospitalizations, and urgent TAVIs (relative reductions of up to 29%, 23%, and 38%, respectively). However, this occurs at the expense of excessive wait times in the nonprioritized group (up to 25 weeks). The reduction in adverse events was lower when the nonprioritized group had more reasonable wait times.

When developing and implementing waitlist prioritization strategies, it is important to consider the resource constraints of the system and the patient profile, as the benefits of providing faster access to prioritized patients can lead to unreasonable wait times for nonprioritized ones. In settings with long wait times, prioritization initiatives must be followed by expansion of supply to achieve optimal improvements in health outcomes.

To meet the needs of an evolving health and care system, the National Institute of Health and Care Excellence (NICE) is changing its approach to topic prioritization so it can focus on what matters most. To support this, NICE ran a public dialogue to gather informed opinion on how it should select topics for guidance, including for some technology evaluation programs.

Fifty-five general public participants from across England took part in two face-to-face and three online deliberative workshops (each lasting two or three hours, held over four weeks in 2023). Participants were asked to consider the following criteria in the context of prioritization: health and care need, evidence availability, system impact, budget impact, health inequalities, and environmental sustainability. The workshops were designed to understand whether any aspects were more important than others and explore the reasons why. They used deliberative engagement methods and included trade-off exercises, role-play, group discussion, ranking tasks, and interactions with specialists.

Emerging findings show that the participants think NICE should consider several aspects when prioritizing topics for guidance. Health and care need was of primary importance for people, followed by evidence availability, budget impact, and system impact. Health inequalities and environmental sustainability were generally considered to be less important, though participants still felt these were areas that should inform NICE’s prioritization decisions. Participants identified relevant interactions between the criteria, suggesting that each criterion cannot be considered in isolation. Full results will be available to present at HTAi 2024.

Deliberative public engagement is a meaningful way to involve the public in complex policy decisions with a social value element. Broad public agreement was found with the criteria NICE has proposed to consider when prioritizing topics for guidance, and some criteria are more important to people than others. The findings will feed into NICE’s new approach to topic prioritization.

One of the pillars of health technology assessment (HTA) is transparency, which guarantees reproducibility and accountability. Due to the “black-boxness” of artificial intelligence (AI) models, the use of AI-based tools adds new layers of complexity for transparency issues. The aim of this scoping review is to map AI-based tools applied in HTA processes, regarding human supervision and “open-sourceness” aspects.

A search strategy using the terms “AI,” “HTA,” and correlated terms was performed in nine specialized databases (health and informatics) in February 2022. Inclusion criteria were publications testing AI models applied in HTA. Selection of studies was performed by two independent researchers. No filter was applied. Variables of interest included a subset of AI models (e.g., machine learning [ML], neural network), learning methods (e.g., supervised, unsupervised, or semi-supervised learning), and code availability (e.g., open source, closed source). Data were analyzed exploratorily as frequency statistics.

ML with one layer of hidden nodes was applied in 48 (78.6 %) studies, while deep learning (DL) (two-plus layers) were applied in eight (13.1 %). ML models that used supervised learning accounted only for half of the reported models, while half used unsupervised learning. Considering supervision methods in DL models, seven used unsupervised learning, and one used supervision. Four studies did not report the AI model, and 14 studies did not report the supervision paradigm. It was not possible to assess “open-sourceness” in 31 studies. Among the identified software, seven models were not open source, and 13 were open source.

Transparency and accountability are of utmost importance to HTA. Complexity of AI models may introduce trustworthiness issues in HTA. Transparency provided by open-source code becomes essential in building trust in the automation of HTA processes, as does quality of report. Although progress has been observed in transparency and quality, the lack of a methodological framework still poses challenges in the field.

The advent of artificial intelligence (AI) in digital health technologies (DHT) requires a comprehensive health technology assessment (HTA) to ensure safety and effectiveness and to demonstrate the value of these technologies in healthcare systems. Recognizing the unique requirements posed by AI-based DHT, our agency has undertaken several initiatives to tailor and adapt our processes for effective HTA.

We started by identifying the processes that were not working optimally and planned a list of actions needed to improve them. These actions were: (i) to develop a new evaluation framework for the assessment of DHT, including those based on AI; (ii) to increase our activity on early HTA; (iii) to seek collaboration with an organization for technical assessment of AI, with a particular emphasis on trustworthy AI requirements; (iv) to adapt our HTA report templates; (v) to create new forms to request information from the technology developers; and (vi) to set up a working group on HTA of AI-based DHT.

We have now an evaluation framework that informs on the relevant aspects for HTA of AI-based DHT and the evidence that developers need to generate in order to proof the value of their technology. We designed a circuit to identify promising technologies and increased our early HTA work for timely advice. The evaluation team now involves an additional partner for the technical assessment domain. In addition, we have new templates for early HTA reports, which explain those AI-specific elements to be addressed, as well as industry information request forms that enable collecting specific information like algorithm type and population used for clinical validation.

Tailoring HTA processes to AI-based DHT is crucial in today’s fast-paced health technology landscape. Our new evaluation framework, the involvement of new partners in the assessment team, the creation of new templates, and enhanced early HTA work helps to evaluate these technologies optimally. We are also setting up a working group to ensure homogeneous evaluation within Spain.

Literature reviews (LR) play a crucial role in all health technology assessment (HTA) dossiers, presenting evidence-based value of interventions. There is global exploration of artificial intelligence (AI) to expedite and enhance the efficiency of literature reviews. Our research aimed to identify any existing guidance from HTA bodies regarding the use of AI for conducting literature reviews.

We conducted a comprehensive search and review of any published guidance from prominent HTA bodies, including the National Institute for Health and Care Excellence (NICE, England), Scottish Medicines Consortium (SMC, Scotland), National Centre for Pharmacoeconomics (NCPE, Ireland), National Authority for Health (HAS, France), Federal Joint Committee (G-BA, Germany), Institute for Quality and Efficiency in Health Care (IQWiG, Germany), Canadian Agency for Drugs and Technologies in Health (CADTH, Canada), and Pharmaceutical Benefits Advisory Committee (PBAC, Australia). This was done to gain insights into their views regarding the utilization of AI in literature reviews. Additionally, we engaged with HTA representatives, such as NICE, to gain a deeper understanding of their perspectives.

We found a lack of clear guidance on the use of AI for conducting LRs. NICE has recommended a priority screening technique using machine learning (ML) for identification of a higher proportion of relevant papers at an earlier stage. NICE is currently in the process of developing guidance and is updating its manual in this area. SMC refers readers to NICE methodologies. In its HRB-CICER report, NCPE only acknowledges the potential of ML algorithms for LRs, with no additional information. IQWiG, in its general methods, recommends the use of ML-validated classifiers for identifying randomized controlled trials (RCTs) within bibliographic searches.

Our research indicates that there is scarce guidance available for the use of AI in LRs for HTA submissions. However, considering the rapidly evolving nature of this field, it is anticipated that guidance documents and manuals will be updated in the near future.

In 2021, the Australian Government Department of Health and Aged Care’s Consumer Evidence and Engagement Unit (CEEU) launched an online consultation hub to provide a centralized pathway for consumer engagement in health technology assessment (HTA) processes. The hub enables consumers (patients, carers, health professionals, and citizens) to provide commentary on items being considered by HTA committees for subsidization.

A survey was developed by the CEEU, committee members, and consumer representatives to facilitate consultation on applications assessed by the Pharmaceutical Benefits Advisory Committee (PBAC)—a principal Australian HTA committee. Questions were designed as simple and engaging, including guidance on the type of information required. Responses are summarized thematically by efficacy, safety, accessibility, and quality-of-life impacts of the proposed health technology. New surveys are launched to coincide with each PBAC meeting agenda publication and allow a ten-week consultation period. Awareness of consultations is supported by the CEEU’s HTA Engage e-newsletter, which alerts the public and targeted stakeholders.

The hub surveys have enabled streamlined consumer commentary to be provided for PBAC considerations. It has also allowed increased time for quality consultation with stakeholders. The success of the hub is further demonstrated in the current development of a similar survey for another principal committee, the Medical Services Advisory Committee (MSAC), to transition its consultation processes to the hub. Of note, while consumers’ feedback on the hub is positive, there remains a desire for educational resources and face-to-face interactions to support consumer engagement in HTA processes. The CEEU are developing materials to address and further support this need.

In a time when technological communication can be optimized to complement face-to-face conversations, it is vital consumer engagement in HTA processes follows suit. To facilitate continued engagement that is sustainable for both present and future Australians, the CEEU continues to evolve a strategy regarding virtual consultations to increase consumer awareness and education and promote effective participation in Australian HTA.

Understanding patient experience and needs is crucial to develop high-value therapies. Patient experience data (PED) inform trial design and evidence generation plans. The U.S. Food and Drug Administration’s roadmap to patient-focused outcome measurement advocates integrating PED into product development. We adapted this theoretical framework to include the health technology assessment (HTA) perspective and operationalized it as a patient disease strategy (PDS) framework.

The PDS framework is a methodology that systematically integrates patient-informed activities to reflect the patient health value of a new treatment. A PDS is developed per indication, initiated in the preclinical phase, applied in clinical development, and continuously adapted throughout the product development lifecycle. The three PDS phases include: (i) development of patient profile, including epidemiology, demographics, patient journey, disease, and treatment burden for patients and caregivers; (ii) PED gap analysis, focusing on identification of patient priorities, unmet needs, preferences, and expectations for new therapies; and (iii) translation into actions, such as diversity and inclusion (D&I) plans and outcomes strategy.

Out of 58 indications, 31 percent have endorsed PDS and 67 percent are in progress. Patient-relevant label opportunities increased by over 50 percent. Each indication was informed on average by patients from three different countries. The PDS framework helped to identify factors that impacted health outcomes for integration into trial designs and D&I plans. Early understanding of heterogeneous patient populations, unmet needs, benefit/risk trade-offs, and patient experiences ensured development programs measured the most meaningful outcomes while also addressing evidence gaps. Early understanding of patient priorities and barriers to participation optimized the studies by reducing burden and identifying proactive support needed to complete the trial.

The PDS framework systematically identified health value opportunities for a target population and integrated the patient needs into the overall development plan. PED informs clinical trial design and endpoint strategy optimization, including factors that influence diversity and data integrity. We anticipate that the PDS will enable HTA decisions to reflect patients’ health value and ultimately improve access to innovative therapies.

The National Centre for Pharmacoeconomics (NCPE) has a Patient Organisation Submission Process (POSP) enabling patients to communicate their experiences to the drug-reimbursement decision-maker. The NCPE proactively invites and supports patient organizations to make submissions to the decision-maker for ongoing health technology assessments (HTAs). We evaluate uptake of the POSP and determine whether submission trends differ by drug type.

We reviewed all HTAs completed by the NCPE since 2016 (when the POSP was first introduced) to present (data cut-off date 22 November 2023). Time trends in the proportions of HTAs for which a patient organization submission had been made to the NCPE were analyzed descriptively. We also compared the proportions of HTAs for which a patient organization submission had been made for (i) orphan versus non-orphan drugs and (ii) oncology versus non-oncology drugs.

The number of patient organization submissions made to the NCPE has increased over time. In 2016, 24 percent (6/25) of completed HTAs were associated with a patient organization submission compared with 50 percent (11/22) in 2023. The proportions of completed HTAs associated with a patient organization submission are comparable between orphan (38%; 24/64) and non-orphan drugs (29%; 34/117) (Chi²; p=0.245). The proportion of completed HTAs for which a patient organization submission had been made was lower for oncology drugs (14%; 14/97) versus non-oncology drugs (52%; 44/84) (Chi²; p<0.001).

Patient organization submissions to the NCPE have increased over time. The proportions of HTAs for which patient organization submissions have been made are comparable between orphan and non-orphan drugs. However, the proportion of patient organization submissions for oncology drugs is lower than the proportion for non-oncology drugs. The NCPE will continue to liaise with patient organizations to increase engagement with the POSP.

Advanced therapy medicinal product (ATMP) is a classification used by the European Medicines Agency (EMA) for cell-, gene-, and tissue-engineered therapies. Since the first ATMP received market authorization in 2009, a substantial proportion of these highly innovative therapies have been withdrawn from the European market. This research investigates the key reasons underlying these withdrawals.

ATMPs with withdrawn EMA marketing authorizations were identified from the EMA website. A targeted review of relevant company press releases was undertaken and key information extracted. Health technology assessment (HTA) outcomes in France, Germany, and England were extracted from their respective websites (1 Sep 2023).

Thirty-two ATMPs have received EMA marketing authorizations, with 22 percent (7/32) withdrawn. One of these seven withdrawals was driven by unfavorable clinical results versus six out of seven due to commercial reasons. Of the six withdrawals driven by commercial reasons, four were associated with negative HTA and/or reimbursement issues (Chondro-Celect, Glybera, and Zalmoxis’s Service Médical Rendu (SMR, actual medical benefit) insufficient, driven by data-related issues in France; Glybera’s challenge to obtain reimbursement by insurance funds with only one sale reported, and Zynteglo’s price agreement not reached in Germany). One of the six withdrawals (Chondro-Celect) was also associated with hospital exemption (continued production without marketing authorization). One of six (Provenge) was also associated with chemistry-, manufacturing-, and controls (CMC)-related issues.

The ATMP landscape has rapidly evolved in the past decade. While ATMPs offer the promise of long-term benefits, the unique manufacturing, clinical, and especially the reimbursement challenges they face can lead to commercial failure. To ensure ATMP patient access and commercial success, manufacturers should engage early with payers, understand potential reimbursement challenges, and proactively plan to mitigate these.

Personalized precision medicine (PPM) is an innovative approach to disease diagnosis, prognosis, and treatment of individual or group characteristics using diverse data sources. While omics technologies are integral to PPM, they pose challenges. Therefore, developing an appropriate methodology to assess these technologies is crucial for patient safety, resource efficiency, and clinical decision-making within the Spanish National Health System.

This health technology assessment (HTA) methodology procedure was developed by combining three different approaches: a systematic review (SR); a survey targeting experts in omics technology, ranging from basic science researchers to clinicians, and patient associations; and, finally, a consensus method (RAND Appropriateness Method [RAM]).

Through data extraction and evidence synthesis of the 38 studies included in SR, 30 existing frameworks for evaluating omics technologies were identified, as well as the elements needed to assess these technologies, leading to the first version of the framework. Two surveys were performed to integrate the perspectives of omics technology experts and patients. Subsequently, this framework version was further developed by a RAM consensus panel of experts from HTA agencies to ensure a rigorous evaluation of gathered data. The final framework was categorized into 94 elements divided into sections, categories, domains, and subdomains.

A methodological guide, including the assessment framework, was developed for the Spanish HTA network. The framework is divided into several sections addressing evidence-gathering, provision models, organizational elements, economic evaluation, and ethical and social implications. Compared to other available frameworks, our proposal included aspects such as bioinformatics, technological maturity level, and the patient perspective with the personal utility domain.

Lung cancer (LC) ranks second globally in neoplasia diagnoses and exhibits the highest oncological mortality rate. Oncology has seen a marked increase in U.S. Food and Drug Administration (FDA) approvals for novel drugs, notably in the case of LC. This study aims to assess the innovativeness of recently FDA-approved LC treatments compared to existing options, spanning the 2011 to 2021 period.

The innovativeness of drugs was assessed through the Pharmaceutical Innovativeness Index (PII). The methodology considers population health needs and aggregated clinical benefits, while weighing methodological quality and the adequacy of available evidence. New drugs are analyzed considering available therapeutic alternatives. The assessment assigns an innovativeness score to the drugs, with 1.0 representing the highest degree of innovation. PII was applied to all drugs with their first FDA indication for LC treatment. The evaluation considered specific clinical indications (e.g., treatment of ALK-positive LC), and data from FDA clinical review reports and pivotal clinical trials were considered.

Eighteen new drugs were identified during the period. The drugs were evaluated with a mean PII of 0.615, ranging from 0.474 to 0.811. No discernible trends in innovativeness were observed within specific indications. Overall, the evaluations indicated that these medications were approved to address significant therapeutic needs. However, the added therapeutic value ranged from absent to moderate. Most supporting evidence for these approvals was derived from clinical studies with low risk of bias. Nevertheless, these studies often featured inadequate designs due to the absence of a comparator arm, limiting the assessment of added therapeutic value.

Pharmaceutical innovations in recent years for LC treatment have demonstrated limited clinical benefits when compared to existing alternatives, highlighting a substantial therapeutic need. The application of the PII can assist in healthcare decision-making, guide investments and research efforts, as well as inform pricing and reimbursement of new technologies, thereby contributing to the sustainability of healthcare systems.

In Germany, organized cervical cancer screening with annual Papanicolaou (Pap) cytology for women age 20 to 34 years and three-yearly co-testing with human papillomavirus (HPV) and Pap for women as of age 35 years is standard. However, about 30 percent of women eligible for screening remain un/under-screened. We systematically evaluated benefits, risks, and cost-effectiveness of offering additional HPV self-sampling (HPV-SS) to non-attendees.

A validated Markov model for the German context was used to evaluate different HPV-SS screening strategies compared to standard clinician-based screening: HPV-SS for non-attendees age 25 to 65, 30 to 65 or 35 to 65 years, every five years with regular invitation, either opt-in (invitation with link to order the test), or send-to-all (test sent with invitation). German clinical, epidemiological, and economic data (index year 2022/23), along with test accuracy and HPV-SS-attendance data from international meta-analyses and trials were incorporated. Outcomes included undiscounted life years gained (LYG) compared to standard screening without HPV-SS in non-attendees, and the incremental cost-effectiveness ratio (ICER; in EUR/LYG). Comprehensive sensitivity analyses were performed.

Incremental undiscounted effectiveness (compared to standard screening without HPV-SS) and discounted ICERs (compared to next effective) for non-dominated HPV-SS screening strategies were 0.00090 LYG (EUR22,700/LYG) for offering with five-yearly screening invitation an HPV-SS (opt-in) to non-attendees age 35 to 65, 0.00166 LYG (EUR25,900/LYG) for HPV-SS (send-to-all) age 35 to 65, 0.00167 (EUR726,000/LYG) for HPV-SS (send-to-all) age 30 to 65, and 0.00167 LYG (EUR1.78 million/LYG) for HPV-SS (send-to-all) age 25 to 65 years. Other opt-in strategies were dominated. Results were robust over a wide range of parameter variations.

Offering HPV-SS (send-to-all) to non-attendees every five years as an additional strategy within the organized cervical cancer screening program is effective and cost effective in the German context. The results can be used to inform decision-makers and clinical guideline developers regarding incorporation of the specific version of HPV self-sampling into the established organized cervical cancer screening program in Germany.

The value of health technologies may not be fully reflected in existing generic preference-based measures (GPBMs). The nine-dimension EQ Health and Wellbeing Short (EQ-HWB-S) is a new GPBM developed to assess health, informal carer, and social-care-related quality of life. The objective was to compare standard and new approaches used to generate preferences for the EQ-HWB-S.

Three feasibility studies that included qualitative work have valued the EQ-HWB-S with members of the public in the United Kingdom: (i) videoconferencing interviews using time trade-off (TTO) and discrete choice experiments (DCEs) from the EuroQol Valuation Technology (EQ-VT) protocol (n=600); (ii) online DCEs using the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method (n=300), an adaptive DCE with a binary search to locate “dead”; and (iii) Online elicitation of Personal Utility Functions (OPUF) (n=300), which is a compositional method with dimension weighting, response level rating and anchoring on dead. Participant/interviewer feedback, data quality, and the weights were compared.

Self/interviewer reported understanding was high (>70%) across all studies. Qualitative findings indicated misunderstanding for some OPUF steps (e.g., the anchoring on dead). Inconsistencies or illogical answers were small in the EQ-VT study (7%) and OPUF (16%). The PAPRIKA study had a priori exclusions criteria (e.g., time taken) that resulted in 44 percent exclusions. Pain, activity, mobility, and sadness/depression were the most important in all the studies. The value of the worst state was −0.384, −0.51, and −0.15 for EQ-PVT, PAPRIKA, and OPUF, respectively, and there were differences in dimension weights (e.g., PAPRIKA gave less weight to mobility but more to cognition).

EQ-HWB-S is a GPBM that provides an innovative approach to measuring and valuing outcomes. Standard and new approaches to eliciting preferences are feasible, but there are differences in the resultant weights. PAPRIKA and OPUF may improve attribute attendance and be more cost effective as they are administered online, but there is scope for improvement to ensure understanding and engagement.

Research with members of health technology assessment (HTA) bodies has uncovered key barriers to integrating patient preference (PP) data into HTA, including concerns about resources/time constraints and a lack of clarity around who is responsible for data generation. We sought to develop a roadmap that addresses these issues, outlining the roles and responsibilities of different stakeholders to foster more sustainable PP data generation.

Based on a forthcoming article to be published in The Patient, this roadmap consists of a step-by-step approach for PP evidence generation. Real-world case studies and literature will be used to illustrate each stage, from identifying priority treatment areas and evidence gaps, forming a steering committee and engaging HTA members, to securing syndicated funding and disseminating results with full transparency.

In contrast to standard approaches to data generation, this roadmap focuses on proactive data collection, collaborating with those who will ultimately use the data (HTA), and pooling resources to mitigate costs and the risk of bias. The roadmap can be applied to all preference-sensitive treatment areas and across health systems/countries. It is designed to be a continuous process, whereby preferences are regularly updated to align with changes to the treatment landscape. A graphic summary of the roadmap is available for viewing at this link: https://cappre.info/images/HTAprocess.pdf

Patient preference data has the potential to make healthcare decision-making more informed, socially legitimate, transparent, and accountable to the patient community. However, current approaches to capturing PP data can be resource intensive with narrow applicability in their findings. The present roadmap offers an alternative, sustainable solution.

Treatment for heart valve diseases (HVD) typically involves surgery, but less invasive procedures are becoming more common. Although the two procedures have similar outcomes, the risk–benefit profiles differ, indicating patients should be included in treatment decisions so they align with their values/preferences. This study aimed to determine patients’ preferences for HVD procedures, and the relative importance of treatment attributes.

An online survey with discrete choice experiment (DCE) was disseminated to patients with aortic stenosis, mitral valve regurgitation, and tricuspid valve regurgitation. Participants were presented with several choice sets, each comprising two hypothetical treatment procedures (labeled “invasive procedure” and “minimally invasive procedure”) as well as an opt-out. DCE attributes were selected based on a literature review, qualitative interviews with patients and specialist doctors, and steering committee consultation (patients, patient organization representatives, and cardiac physicians). Responses were collected via healthcare recruiters, online panels, and patient organizations. DCE data from 143 Australian patients was analyzed using a mixed multinomial logit (MMNL) model.

Results indicate an “experience effect” whereby patients preferred the same type of treatment they had undergone previously. For example, patients who had undergone a transcatheter procedure were more likely to choose the minimally invasive procedure in the experiment and vice versa for those who had undergone invasive procedures like open-heart surgery. Patients were willing to switch procedures based on its risk–benefit profile, and most patients preferred the minimally invasive procedure when it reflected the profile of transcatheter aortic valve replacement (TAVI), even if they had previous invasive procedures experience. Key attributes driving choice were valve durability and regaining independence.

There is a great deal of heterogeneity in HVD patient preferences, even when treatment outcomes appear similar. Patients preferred a minimally invasive procedure over an invasive procedure, irrespective of prior treatment experience with valve durability and independence driving choice. These results can inform healthcare decision-makers about what features of HVD procedures patients value most, taking into consideration patients’ prior experiences.

There are significant delays in the funded access to medicines. Studies indicate that in many countries it takes more than a year for patients to have funded access to medicines after market authorization. This study aimed to understand the disparities in timelines for funded access to medicines across different countries and to identify underlying reasons for this access gap.

We conducted a scoping review to examine the nature of health technology assessment (HTA) processes, current methods, and policies for medicines in ten jurisdictions. The jurisdictions included in this study are Australia, Canada, France, Germany, South Korea, the Netherlands, United Kingdom (divided into England, Scotland and Wales), and United States of America. The information was extracted from the websites of International Network of Agencies for Health Technology Assessment (INAHTA) member agencies in the selected jurisdictions, grey literature from governments’ websites, and peer-reviewed literature.

Overall median time from submission of the evidence dossier to HTA recommendations for most jurisdictions is 22 weeks. Although there are similarities in the time taken to reach a funding decision, there are considerable variations in the time taken for patients to have funded access to medicines after HTA recommendations. Only a few countries mentioned a specific timeline within which medicines approved for funding should be listed. Time taken for price negotiations and other arrangements (i.e., risk-sharing agreements) may contribute to varying timelines for listing medicines for funding. Mostly, such negotiations are confidential and may not be time limited.

There was surprising consistency, globally, in the time it takes for funding decisions after medicines registration. The causes of delays in the medicines’ listing decisions are multifactorial and mostly occur after HTA recommendations. The parallel regulatory-assessment process and prioritization tend to reduce the time to a funding decision. However, transparency is needed in the listing process to improve overall timeliness.

The research included a rapid review of current literature to describe epidemiology, management, and system impact of heart valve disease (HVD) in adult populations. Key issues were identified in consultation with expert focus groups and were framed across the continuum of care and systemic policy issues. The groupings were adapted and adjusted during deliberations.

A rapid literature review was conducted on HVD key interventions and evidence of effectiveness along with expert interviews to identify high-level themes for reform. This served as the evidentiary backgrounder. Two virtual policy engagements with clinical leaders, patients, and health system managers were conducted. The focus was their collective drafting of recommendations. These workshops identified nine thematic areas and developed associated recommendations for action under each theme. The success of the process is evident as the report has been taken up as a roadmap for ongoing research and policy work.

A comprehensive grouping of recommendations for improving HVD detection, management, and treatment in Canada was produced. It was designed to be comprehensive to then allow more targeted work to proceed under an evidence-informed and clinically endorsed agenda.

Heart valve conditions are increasingly treatable, especially if detected early. Innovation in treatments as well as detection and management to address gaps in care were identified as the most urgent priorities. The key result was formation of formal working groups with a professional society to explore spoke–hub-and node models for care delivery and to launch awareness programs for early detection.

Ghana’s reference case, developed to guide the conduct of economic evaluation as part of health technology assessment (HTA) guidelines, recommends the conduct of cost–utility analysis using outcomes such as quality-adjusted life years (QALYs). There is no national value set available for the Ghanaian population to be used in estimating QALYs. This study aimed to develop a value set for Ghana using the EuroQol 5-dimension 5-level questionnaire (EQ-5D-5L) instrument.

Face-to-face preference data were collected from 300 adults across three regions of Ghana using the adapted version of the EuroQol Valuation Technology (EQ-VT) standardized valuation protocol developed specifically for EQ-5D-5L valuation studies using composite time-trade-off (cTTO) and discrete choice experiments (DCEs). Different preference models were generated using both the cTTO and DCE data, individually or together to provide complementary results on respondents’ utility preferences. Models explored include generalized least squares, tobit, heteroskedastic, logit, and hybrid. The best-fitting model was selected for the value set based on its logical consistency, ability to account for left-censored and heteroskedasticity data, and statistical significance of parameters.

The 300 interviews provided 4,500 cTTO responses and 4,200 DCE responses. The demographic characteristics of respondents were representative of the Ghanaian population for religious background, level of education, and marital status. The preferred model chosen for the Ghana value set was hybrid tobit, random effect heteroskedastic, constrained model. The predicted value for the worst attainable health on the EQ-5D-5L (i.e., health state 55555) was −0.493 and that of the best health state (11112; except full health) was 0.969. The largest decrement was registered for level five mobility (0.369) followed by pain/discomfort (0.312), self-care (0.273), anxiety/depression (0.271), and usual activities (0.268).

This is the first Ghanaian EQ-5D-5L value set based on social preference derived for a nationally representative sample in Ghana. The value set will play a key role in the institutionalization of HTA in Ghana and the use of economic evaluation studies to inform priority setting where different health technologies can be compared. A planned findings dissemination to stakeholders is underway.

Health technology assessment (HTA) agencies and researchers recognize the necessity of evidence-based methodologies beyond quantitative data to assess feasibility, appropriateness, meaningfulness, patient values, preferences, acceptability, and equity. Despite existing guidelines for synthesizing qualitative data, the HTA framework requires clarification. This review aims to describe the frameworks, tools, and processes used to synthesize qualitative evidence and assess the quality of HTA.

Using the JBI methodology, the authors accessed databases such as MEDLINE, LILACS, CINAHL, Embase, Web of Science, Scopus, PsycINFO, Cochrane Library, JBI Database, and ScienceDirect. Grey literature searches included ProQuest, OpenGrey, CADTH’s Grey Matters, Google Scholar, and HTA agencies’ websites. Inclusion criteria focused on synthesizing qualitative evidence frameworks, methods for evidence synthesis, and quality rating. The review had a global scope, without specific population and time restrictions. Data, encompassing fundamental concepts, frameworks, methods, subjects, and objectives, were presented in tables and figures.

Out of 2,054 articles, 31 were included, mainly from Europe, with a predominant “guide” authored by an HTA agency and university. The majority of documents did not originate from agencies. Only three agencies developed specific documents. A surge in publications occurred in 2018/2019. Qualitative data in HTA were justified for opinions, acceptability, feasibility, and equity. SPICE was the most cited acronym; RETREAT was the preferred framework. Thematic synthesis was the most cited method, CASP for quality assessment. GRADE-CERQual graded evidence quality, and ENTREQ was cited for reporting qualitative research. The GRADE EtD framework was the sole tool mentioned for recommendations.

This review highlights a growing trend in including qualitative evidence in HTA. While various proposals suggest instruments and methods, few documents cover all necessary steps, resulting in diverse recommendations. Standardizing processes can improve decision-making by guiding the integration of qualitative evidence, potentially enhancing recommendation quality. This ensures evidence on feasibility, appropriateness, significance, patient values, preferences, acceptability, and equity are considered.

Costs for pharmaceutical products are increasing. Pharmaceutical companies claim that high research and development (R&D) costs are the reason for the steep price increase of new products. However, there exists little data to support such claims; there is a lack of transparency in R&D cost reporting. This research intends to analyze and to disaggregate the costs of pharmaceutical R&D.

Studies on the costs of introducing new medications to the market can differ substantially in their methodology, their origin of data, and their results. A scoping review was conducted on costs of R&D for pharmaceutical products using Embase, PubMed, and EconLit, using a combination of the terms “drug research and development” and “costs” or “drug research and development” and “expenditure.” Additionally, semi structured interviews with 16 experts from non-governmental organizations (NGOs), pharmaceutical companies, academic researchers, and not-for-profit pharmaceutical companies were conducted to identify the main driving factors for rising costs of new drugs.

Out of 24 studies that analyzed mixed therapeutic fields, the five highest cost estimates had affiliations with industry or received funding from pharmaceutical companies. Non-affiliated researchers are unable to reproduce studies that use confidential data and therefore cannot check the validity of the results. Additionally, different definitions for R&D make analyzing costs challenging. The interviewees emphasized that driving factors influencing costs for pharmaceutical R&D are therapeutic indication, drug complexity, number of patients in clinical trials, length of the development process, and attrition rates.

Due to the diverse nature of drug development and the confidential information held by pharmaceutical companies, it is a challenge to provide an exact assessment of the average costs of pharmaceutical R&D. Transparency policies with well-defined definitions for R&D are necessary to level the information asymmetries between the private and the public at price negotiations.