IDegLira is a fixed-ratio combination (FRC) comprising insulin degludec and the glucagon-like peptide-1 receptor agonist (GLP-1RA) liraglutide, used for treating uncontrolled type 2 diabetes mellitus (T2DM) patients. We aimed to compare the long-term clinical and cost outcomes between IDegLira and basal-bolus insulin therapy by analyzing data from the Chinese healthcare system.

The IQVIA Core Diabetes Model was employed to compare the two treatments. Cohort characteristics were derived from DUAL II China in patients with uncontrolled T2DM on basal insulin. Efficacy data were sourced from a published indirect comparative analysis. Utilities, medication, and complications costs were taken from publications. All costs were calculated in Chinese yuan (CNY) and discounted to 2022. The long-term outcomes estimated by the model spanned 40 years and encompassed life years (LY), quality-adjusted life years (QALY), cumulative incidence of diabetes-related complications, time alive and free of complication, costs, incremental cost-effectiveness ratio (ICER), and a probabilistic sensitivity analysis.

IDegLira was associated with increased LY (12.46 vs 12.413), increased QALY (10.946 vs 10.669), and reduced costs (CNY408,580.94 [USD56,407.22] vs CNY586,937.81 [USD81,030.54]) compared to basal-bolus insulin therapy. IDegLira was dominant compared to basal-bolus insulin therapy and exhibited a reduction in the cumulative incidence of diabetes-related complications. Additionally, IDegLira extended the time alive and free of complications. The probabilistic sensitivity analysis showed with 100 percent accuracy that IDegLira is more cost effective as compared to basal-bolus insulin therapy.

Based on this analysis, IDegLira can be fully regarded as a dominant treatment compared to basal-bolus insulin therapy in Chinese patients with uncontrolled T2DM on basal insulin.

Diabetes has been shown to influence the individual’s work productivity in terms of both presenteeism (reduced productivity while at work) and absenteeism (absence from work because of illness). We sought to estimate the potential productivity gains associated with the modification of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) over the next 10 years in Australia, from 2023 to 2032.

Dynamic models were structured to estimate productivity-adjusted life years (PALYs) lived by Australians aged 20 to 69 years. The model simulation was first undertaken assuming currently expected trends in the incidence of myocardial infarction (MI) and stroke in T2D (original cohort), as calculated using the 2013 PCE-ASCVD algorithm. Subsequent models were then re-simulated using hypothetical scenarios that reflected the potential benefits of CVD reduction using published trials. The model was also repeated assuming that the original cohort has no CVD. Differences in PALYs lived by the “original cohort” and the different cohorts with reduced CVD risk reflected the PALYs gained. Sensitivity analyses were conducted.

Using data from published studies, the model assumed a reduction of 50 percent in systolic blood pressure (SBP), a reduction of 50 percent in smoking, 50 percent increase in high-density lipoprotein cholesterol (HDL), and a reduction of 35 percent in incidence of T2D. Over the working lifetime, from 2023 to 2032, reducing SBP, smoking, and incidence of T2D led to the gain of 140,105, 333,127, and 998,805 PALYs, respectively. Further, increasing HDL and assuming the original cohort with T2D has no CVD are expected to lead to the gain of 71,623 and 889,455 PALYs, respectively. Sensitivity analyses confirmed the robustness of study findings.

The impact of CVD as a complication from T2D on work productivity is significant. Screening and prevention strategies tailored early in life are likely to exert a positive impact on health and work productivity.

Disease-modifying dementia treatments (DMDTs) target amyloid beta or tau proteins and have the potential to change disease progression, representing a step change in the management and treatment of Alzheimer’s disease. Given the novel mechanism of action and impact on health care, the NICE Health Technology Assessment Innovation Laboratory (HTA Lab) sought to identify and contextualize the key issues for future appraisals.

We reviewed published assessment reports of DMDTs from international HTA agencies and conducted a scoping review of published economic models of pharmacological treatments to understand the challenges associated with evaluating the cost-effectiveness of dementia treatments. The HTA Lab held an engagement workshop with 27 external stakeholders, including expert clinicians, implementation partners, health economists, and representatives from international agencies to discuss and confirm the key issues and considerations likely to emerge during an appraisal of DMDTs.

Key clinical and cost-effectiveness issues were identified and discussed. We concluded that consideration needs to be given to the diagnostic methods to identify the DMDT-eligible population in the UK, the validity of the surrogate outcomes used in the DMDT clinical trials, treatment effectiveness in different populations, and the incidence of DMDT-associated adverse events. Economic considerations include the type of economic model used in the appraisal, modeling the natural history of the disease, paucity of quality-of-life data in the treatment population, the inclusion of societal impact, treatment duration, stopping rules, and long-term effectiveness beyond the clinical trials.

DMDTs could have the potential to transform Alzheimer’s disease care. With multiple treatments on the horizon, the appropriateness and acceptability of the new mechanism of action underpinning these treatments should be considered. We have identified areas of uncertainty that are likely to arise during an appraisal process, to facilitate the timely approval of these medicines and patient access.

The use of real-world evidence (RWE) by health technology assessments to evaluate emerging technologies has increased. Although traditionally recognized as the gold standard of evidence, randomized controlled trials (RCTs) may be challenging to conduct, especially in surgical settings. The aim of this analysis is to synthesize and compare results from RWE with those from RCTs for robotic-assisted surgery (RAS).

A systematic review was performed according to PRISMA methods. RWE and RCT studies published between 1 January 2010 and 31 December 2022 and comparing RAS, laparoscopic, or open surgery across seven procedures were included. Perioperative outcomes of interest were operative time, length of stay (LOS), conversion to an open procedure, estimated blood loss (EBL), blood transfusions, readmissions, reoperations, postoperative complications, and mortality. A meta-analysis was performed using R software.

Thirty-three RCTs and 121 RWE studies were included. For RAS versus laparoscopy, RCTs and RWE were concordant for conversions (RCT:OR=0.56 [0.42, 0.74], p<0.01; RWE:OR = 0.41 [0.36, 0.47], p<0.01) and LOS in favor of RAS (RCT:WMD = −0.66 [−1.12, −0.20], p<0.01; RWE:WMD = −0.50 [−0.63, −0.36], p<0.01), while operative time was longer for RAS (RCT:WMD = 27.89 [12.66, 43.12], p<0.01; RWE:WMD = 28.89 [15.56, 42.22], p<0.01). RWE complemented RCTs on blood transfusions and mortality, showing RAS favored over laparoscopy. For RAS versus open surgery, RCTs and RWE agreed RAS had significantly lower EBL (RCT:WMD = −260.42 [−515.16, −5.67], p = 0.05; RWE: WMD = −328.27 [−474.08, −182.47], p<0.01), lower postoperative complications (RCT:OR = 0.70 [0.50, 0.97], p = 0.03; RWE:OR = 0.56 [0.50, 0.62], p<0.01), shorter LOS (RCT:WMD = −1.88 [−3.12, −0.64], p<0.01; RWE:WMD = −1.95 [−2.20, −1.70], p<0.01), and longer operative time (RCT:WMD = 35.38 [2.14, 68.61], p=0.04; RWE:WMD = 38.80 [24.62, 52.97], p<0.01). For the remaining outcomes, RCTs showed no difference, while RWE provided complementary results in favor of RAS.

RWE confirmed many of the results shown in RCTs and complemented findings for perioperative outcomes. Based on these results, RWE can supplement the findings from RCTs in the literature, provide more generalizability, and offer a more comprehensive landscape of the evidence on robotic-assisted surgery.

Ranked among the top 10 global health threats, antimicrobial resistance (AMR) prompts concern. Embracing a living evidence synthesis (LES), we address this concern by informing health decision-makers about molecular tests for swift detection of multidrug-resistant organisms (MDROs). This updated HTA report for the Spanish National Health System prioritizes decisions based on the most current evidence, adapting to emerging technologies and evidence.

The LES commenced with a baseline synthesis, shaping the initial HTA report regarding rapid molecular test impact on safety, efficacy, effectiveness, and patient outcomes in suspected infection cases. Based on this, on 12 July 2023, we initiated a 12-month evidence monitoring process. Utilizing the Living Evidence to Inform Health Decisions (LE-IHD) framework and interactive tools, we conducted ongoing baseline synthesis and evidence tracking. Artificial intelligence (AI) and the Living Overview of Evidence (L.OVE) platform aided in continual evidence identification. Tri-monthly scans of trial registries unveiled ongoing studies. New eligible studies were rigorously assessed. Updates in HTA conclusions were in line with this synthesis.

The baseline synthesis identified 25 systematic reviews that suggested that the use of rapid molecular test for the identification of pulmonary tuberculosis showed good performance, but less evidence or low-quality evidence was available for other medical conditions or on patient outcomes. During the conference, we will report on 11 months of monitoring and regular updates, including key messages on changes in the evidence synthesis conclusions whenever there are substantial updates of the HTA report.

The living evidence approach enables timely updates of conclusions for HTA reports relying on low- and very low-quality evidence, enhancing their significance in decision-making. The LE-IHD framework streamlines tasks for HTA developers in planning and executing LES to inform health decisions.

The past decade has seen an exponential increase in peer-reviewed clinical research literature. Consequently, preparing and updating systematic literature reviews (SLRs) is more resource intensive and costly. Artificial intelligence (AI) could potentially accelerate SLR preparation. This study presents a review of evidence evaluating the accuracy of AI methods in SLR preparation and results of a case study using DistillerSR’s AI functionality.

The review was based on a search of MEDLINE, Embase, and Embase Preprints databases using title/abstract keywords and subject heading synonyms for AI, machine learning, natural language processing (NLP), and publication screening and selection. The protocol is published on PROSPERO (CRD42023452391). To supplement this review, we conducted a case study with DistillerSR’s AI tools. We applied the AI classifiers, which use NLP to learn patterns from multiple SLRs across several indications, which encompassed over 15,000 references’ titles and abstracts. We then compared those patterns with the human responses to build an AI model that can be applied to other references.

The search identified 2,209 records. After deduplication, the titles/abstracts of 2,200 records were screened; of these, 79 full-text records were assessed. A total of 42 records met the eligibility criteria for inclusion. The majority were case studies. The most frequently reported tools were DistillerSR AI (n=9), Abstrackr (n=6), ASReview (n=2), and LiveSTART (n=2). The evidence showed efficiency gains, but accuracy varied across studies and AI tools. Results of the case study using DistillerSR’s AI tools indicated efficiency gains with adequate accuracy but with variability across different SLRs. Inclusion and exclusion of articles were consistent with the human decisions.

The findings of our review and case study indicated that AI can be used reliably in the screening of articles for SLRs and could improve efficiency. However, the evidence is still evolving, and additional studies are needed. There is a need for clear guidelines on the role of AI in study screening and selection for health technology assessments SLRs and submissions.

Health technology assessment (HTA), by investigating clinical, economic, and social consequences of technologies in a country, enhances health system equity and sustainability. In low- and middle-income countries (LMICs), economic constraints and inadequate access to specialized human resources present challenges. Therefore, strategies to optimize resource allocation in the health sector are necessary.

A literature review was carried out, with studies that directly identified barriers or facilitators for the use of artificial intelligence (AI) in HTA being considered eligible. The texts were analyzed from the perspective of LMIC. The searches were carried out on 8 August 2023 using the following databases: MEDLINE via PubMed, Web of Science, and Google Scholar. The selection was performed in two stages: (i) screening by title and abstract and (ii) evaluation of the eligibility criteria in full text.

After conducting the search, five studies were selected for narrative synthesis. Evidence of the potential benefits of using AI in HTA in low- and middle-income countries includes rationalization of resources; reduction of the burden on health systems and minimization of human workload; efficiency in data analysis, including clinical data; prediction of economic impact; and support for managerial decision-making. However, important challenges were also raised, such as the deficiency of local infrastructure; the training and education of professionals; the lack of ethical regulation; and the organizational and political considerations of these countries.

There are few studies in the literature that provide scientific support on the use of AI in HTA decision-making in LMIC. The evidence points to increasing the efficiency and rationality of resources, enhancing the results arising from HTA. With this, it is expected to expand access to health technologies and enable more sustainable health systems.

The ever-increasing number of new and innovative digital health technologies (DHTs) also sets new demands on health technology assessment (HTA) methods in addition to traditional HTA domains. In 2018, the Finnish Ministry of Social Affairs and Health recognized the need for new HTA methods for DHTs in Finland and commissioned method development.

The development work of the new HTA method for DHTs and the findings related to it were based on three substudies:

(i) The new HTA method was developed through a literature review, expert interviews, and four multiprofessional workshops.

(ii) Feedback about new HTA recommendations was collected from healthcare decision-makers through a web-based survey (n=24). Feedback on the developed HTA framework was collected through a web-based survey for companies offering DHT products (n=8).

(iii) Initial experiences about the state of data security and protection of assessed products were gathered through the assessment process.

A new Digi-HTA method that supports a wide range of DHTs, such as health apps, AgeTech, artificial intelligence, and robotic solutions, was published in 2019. According to the healthcare decision-makers participating in the study, although the Digi-HTA recommendations included clear and beneficial information, their integration into healthcare decision-making processes should be improved. Responses from companies offering different DHTs indicated that the Digi-HTA framework would be an appropriate tool for performing assessments for their products. During the assessments, deficiencies in compliance with the best practices of data security and protection as well as data security problems were found.

The rapid development of DHTs requires that the HTA methods also adapt to the development so that no new and innovative products are excluded from the assessments. In addition to the value of DHTs, their quality, such as data security and protection, should be assessed so that decision-makers can be supported in the best possible ways.

In Italy, a fixed proportion of health spending is allocated to pharmaceutical expenditure. While the main objective of setting a budget for pharmaceuticals is to control spending, the effectiveness of this ceiling is questionable. This study aims to investigate the determinants of pharmaceutical expenditure for orphan drugs and gather information for effective planning and programming of pharmaceutical spending.

Data analysis relied on pharmaceutical companies’ pricing and reimbursement (P&R) dossiers submitted to the Italian Medicines Agency (AIFA) for drug-reimbursement approval, along with AIFA’s internal procedural documents. The study encompassed all rare disease drugs reimbursed from January 2013 to January 2019. For each drug, a comparison was made between the expected post-negotiation expenditure and the actual spending observed over the three years following reimbursement approval. Potential determinants of the normalized ratio between observed and expected spending were identified using univariate and multivariate beta regression models. The same methodology was replicated to identify potential determinants of the difference between expected spending before and after negotiation.

Fifty-two rare disease drugs admitted for reimbursement during the study period were analyzed. The median expenditure in the first three commercialization years was 7.6 percent lower than the expected post-negotiation spending. Beta regression analysis indicated a significantly lower reduction for innovative drugs (β 0.736, p-value 0.011 univariate, β 0.585, p-value 0.045 multivariate). Similar effects were observed for P&R procedures (β 0.902, p-value 0.007) and the number of indications presented (β 0.754, p-value 0.021), but only in univariate model. Beta regression analysis for the expected expenditure ratio before/after negotiation revealed a significant effect only for the payment-by-result variable (β 1.485, p-value 0.001).

Observed expenditure for orphan drugs aligns with the expected spending post-negotiation. However, in the subgroup of innovative orphan drugs, the observed pharmaceutical spending was higher than estimated. This could be attributed to prescriber preferences and to a prevalent patient pool awaiting innovative treatment. It appears that the recognition of innovativeness favors orphan drugs that are rewarded with faster market access.

Budget impact analyses for the treatment of rare diseases are especially important for the sustainability of health systems due to high treatment costs and uncertainties in target population estimates. The objective of this work is to analyze the elements that influence discrepancies between predicted and observed budget impacts for enzyme replacement therapies for rare diseases in Brazil’s public health system.

All enzyme replacement therapies for rare diseases evaluated by the National Committee for Health Technology Incorporation in the Brazilian Public Health System (Conitec) and with at least one year of use were included. For each technology, the following were identified: number of patients, median patient weight, annual quantity of medication, unit price, and budget impact. The attributes were compared between previous estimates and real-world observation after use. The data sources were publicly accessible administrative databases and Conitec technical reports.

Five technologies were selected: elosulfase alfa, alglucosidase alfa, idursulfase, laronidase, and galsulfase. In the first year, the difference between the estimated and the observed number of patients treated was up to 15 percent lower or higher for four technologies, but with monthly fluctuation throughout the year. The median weight of users was between 23 percent and 468 percent higher for three technologies. The observed price was as expected, with variations between three percent lower and 14 percent higher. The quantity of medicines used was lower (between 39% and 46%) than expected for all technologies. The observed budget impact was 37 percent to 47 percent lower than estimated.

Real-world budget impact was lower than expected for all technologies. The main cause of discrepancies was the estimate of the annual amount of medication, which did not consider gradual adherence and discontinuation of treatment. This highlights the need to review the budget impact methodology for rare diseases, forecasting monthly market share and treatment discontinuation rate.

Comprehensive genomic profiling (CGP) identifies many targets at once. However, it is challenging for reimbursement decision-makers to incorporate all potential effects in their assessment. The aim of this study is twofold: first, to identify which factors, besides effectiveness and costs, might influence the choice for CGP in advanced cancer patients, and second, to identify the available evidence for these factors.

We performed a systematic literature review in MEDLINE, Embase, and Scopus with a two-step design. First, a scoping search was performed to identify relevant factors. Extracted factors were grouped with domains of the EUnetHTA core model and ISPOR (Professional Society for Health Economics and Outcomes Research) “value flower.” Two expert sessions were held to validate factors and construct definitions. Second, a systematic search was conducted to identify the available empirical evidence for these factors. Eligibility criteria for the systematic search were the use of CGP (≥200 genes), advanced cancer patients, and the presentation of empirical evidence on one of the factors.

Five factors were identified in the scoping search: “feasibility” (adopting tests in the health care system), “test journey” (pathway from requesting tests until reporting of results), “wider implications of diagnostic results” (impact of test beyond identifying on-label treatments), “organization of laboratories” (organization of tests and access to tests), and “scientific spillover” (learnings of testing). Eighty-three articles were included following the systematic search, and empirical evidence was identified for the factors “test journey” and “wider implications of diagnostic results”. Few studies had adequate comparative study designs. Heterogeneity was observed among studies in the definitions of outcomes and the reported evidence.

Comprehensive reimbursement decision-making for CGP can be supported by including the five identified factors. However, quantifiable evidence was only identified for the “patient test journey” and “wider implications of diagnostic results”. Current literature provides limited high-quality evidence to establish the added benefit of CGP, as adequately designed comparisons are lacking. For evidence-based decision-making, uniform outcome measurements are recommended.

Patient experience data (PED), encompassing patient preferences (PP), patient-reported outcomes (PROs), and patient input, play a pivotal role in understanding patient needs and informing healthcare decision-making, including reimbursement decisions. This study aimed to assess the current barriers hindering the integration of PED into practice and its particular challenges, opportunities, and concrete policy actions for the systematic implementation of PED.

Semistructured interviews (n=38) were conducted with industry (n=12), non-profit organizations and academia (n=4), regulatory authorities (n=6), health technology assessment (HTA) bodies and reimbursement agencies (n=6), and patient organizations (n=10) in Europe. A thematic analysis was conducted to explore stakeholders’ perspectives and to gain a comprehensive understanding of challenges and opportunities related to the systematic implementation of PED. Interview transcripts were analyzed using the thematic framework analysis to extract and elucidate the insights provided by the diverse stakeholders.

HTA and reimbursement interviewees agreed on the value of including quality-of-life data, particularly when assessed using validated PRO measures. Despite acknowledging the potential of PP, there remained reluctance to integrate PP into reimbursement decision-making. Participants expressed divergent opinions regarding who should collect PED, with some regulators favoring industry, while HTA and reimbursement agencies emphasized transparency and independent PED collection. Limited experience in assessing PED also contributed to hesitancy, underscoring the need for more guidelines, especially at the national reimbursement level. Stakeholders endorsed collaboration through joint scientific consultations, expressing optimism about the impact of the Regulation (EU) 2021/2282 on health technology assessment (HTAR).

This study emphasizes the high potential of PED in informing reimbursement decision-making, fostering a more patient-centered approach. Stakeholder disparities highlight the complexity, necessitating more guidance, scientific robustness, transparency, and collaboration. In light of these stakeholder considerations, the upcoming HTAR holds promise to enhance the systematic implementation of PED, aligning healthcare decision-making with patients’ needs and preferences.

Time to reimbursement has been described as a hurdle to availability of new medicines to European patients, with assessment and decision-making processes frequently quoted as taking the majority of time. In light of the upcoming Regulation (EU) 2021/2282 on health technology assessment (HTAR), the aim was to examine timelines and health technology assessment (HTA) recommendations for orphan drugs in Ireland.

The study reviewed all orphan drug submissions to the National Centre for Pharmacoeconomics (NCPE) from January 2020 to December 2023 inclusive. The number of days from marketing authorization to rapid review (RR) commissioning was calculated. The RR and HTA recommendations were identified for all medicines. The timelines for the RR and HTA process were evaluated.

Of the 66 submissions identified, 38 percent were made prior to marketing authorization (MA), eight percent were made within 30 days of MA, and 79 percent were made 30 days post MA. RRs were completed within 32 days (mean). Full HTA was recommended in 62 percent (n=41). Price negotiations were recommended in 38 percent (50% of which have been reimbursed to date). Where a full HTA was recommended (n=41), 20 have been completed to date (price negotiations were recommended in 90%). For those 20 HTAs completed, 11 have been reimbursed to date; a decision is pending for the remainder. HTAs were completed within 200 days (mean).

Data shows that the majority of submissions were made 30 days post MA. A pragmatic approach may have to be taken nationally to accommodate the HTAR post 2024 and those submissions that are made prior to publication of a joint clinical assessment. The majority of orphan drug HTA recommendations lead to reimbursement recommendations.

Introduced in July 2021, early access to pharmaceuticals allows French patients to have rapid and reimbursed access to presumed innovative medicines prior to marketing authorization or regular reimbursement registration. The French National Authority for Health (Haute Autorité de Santé [HAS]) is responsible for granting early access authorizations and for health technology assessment upstream of regular reimbursement. This analysis evaluates the outcomes of the 2021 reform.

In collaboration with the national regulatory body (ANSM) and the Ministry of Health, a descriptive and retrospective analysis has been conducted to assess the impact of this reform. The data were extracted from published HAS decisions and from the HAS internal information system (EVAMED). The period considered is the first two years of the reform, from 1 July 2021 to 30 June 2023. Different information has been analyzed, such as conclusions of decisions returned, therapeutic areas concerned, and decision processing time. An additional analysis focused on HAS opinions on regular reimbursement for the medicines for which early access was granted.

In two years, over 250 applications (including first applications, modifications, and renewals) have been submitted, increasing over time, and 180 decisions were issued by HAS within this period. Eighty percent of HAS decisions were positive for early access. Among the medicines for which early access was granted, 86 were also assessed for regular reimbursement, of which 78 percent obtained a positive clinical added value score (at least ASMR IV). Overall, the early access program is estimated to have facilitated coverage approximately nine months before regular reimbursement and benefited over 100,000 patients.

Manufacturers make extensive use of early access applications. A positive assessment of the reform outcomes emerges from this analysis, emphasizing the role of HAS in accelerating patient access to presumed innovative treatments. In light of US experience and of the uncertainties surrounding fast-track authorization procedures, monitoring of the French early access system will continue.

The 2021 reform of early access (EA) for medicinal products in France includes a mandatory data collection for each patient. The objective of this work was to perform a two-year review of the protocols for therapeutic use and data collection (PUT-RD) validated by the health technology assessment body (Haute Autorité de Santé [HAS]) in collaboration with the national regulatory body where applicable.

The minimum dataset defined in the PUT-RD varies depending on the regulatory status of the product and may include patients’ characteristics, conditions of use, efficacy, and safety data. A descriptive review of the PUT-RDs approved between 1 July 2021 and 30 June 2023 was carried out by HAS. The following information was retrieved in each published PUT-RD: type of EA (pre- or post-marketing authorization); collection of efficacy data (yes or no) (if yes, inclusion of a patient-reported outcome measure [PROM] or no); type of data source (registry or electronic platform provided by a contract research organization [CRO]).

During the review period, a total of 98 PUT-RDs were validated, corresponding to 98 EA favorable decisions. EAs were authorized prior to marketing authorization in 40 percent of the cases. A collection of follow-up efficacy data was planned in 52 PUT-RDs, including integration of patients’ perspectives through a PROM in 32 PUT-RDs. Data collection through a registry was planned in six PUT-RDs (using the DESCAR-T registry), otherwise data collection was mainly planned through private electronic platforms operated by CROs (80% of the PUT-RDs).

To optimize the generation of relevant clinical data to be used in the regular HTA process, EAs should be requested early, prior to marketing authorization. Additionally, the use of existing data sources, in particular disease registries, is to be developed to avoid duplicate data collection and alleviate the burden for healthcare professionals.

Preadoption assessments are the most frequent type of evaluation conducted by the Spanish Network of Agencies for Health Technology Assessment and Services of the National Health System (RedETS). RedETS aimed to develop a framework to utilize real-world data (RWD) to better adjust its assessments to the Spanish population’s context and provide live assessments throughout the lifecycle of health technologies.

A working group within RedETS was set up, which held several meetings to define the primary uses of RWD in the short term. Next, a manual review was conducted on national and international initiatives that provided guidance on the use of RWD in HTA. Common pathways for utilizing RWD in HTA were identified. The working group agreed to outline and explain the key overarching steps and provide general guidelines for working with RWD, developing as illustration a use case for an interventional technology. The Big Data project of Aragon (BIGAN) was chosen as the data source for the use case.

We formulated a case for leveraging RWD in the assessment of implantable cardiac defibrillators (ICDs) for the prevention of sudden cardiac death (SCD). Based on this scenario, we developed a methodological framework outlining a workflow consistent with RedETS practices. RWD complemented the usual process of systematic review of a technology. Crucial steps comprised the definition of data requirements through a data model specification, an exploratory data analysis, and the construction of a decision model. We presented solutions for dealing with unavailable data on essential variables and unstructured records. We discussed the main limitations to account for when working with RWD.

The task ahead holds great hope but requires overcoming some challenges to fully deploy RWD-driven methods. This entails fostering collaboration with health authorities and designated data holders to address data access challenges. In the short term, it is essential to include data scientists in assessment teams and provide appropriate capacity-building to encompass RWD tools and modeling techniques.

Mental illnesses during pregnancy and after birth (perinatal period) affect one in five mothers and one in ten fathers. They can have adverse implications for child health and development and come with substantial economic costs. Many countries have developed integrated care models. We aim to summarize the international care model recommendations and contrast them with the current situation in Austria.

We conducted a scoping review of guidelines and recommendations for perinatal and infant mental health care (PIMHC) models and synthesized them into a best-practice model. The model overview targets prevention, early identification, and care of perinatal mental illness (PMI), as well as professional groups involved. We identified available Austrian PIMHC services via online search and expert consultation, clustered them according to our international model structure, and visualized availability on a geographical map. Additionally, we analyzed the use of five core mental health benefits from health insurance data. We narratively contrasted international recommendations with the Austrian availability and use of services.

International recommendations suggest integrating primary prevention, systematic screening, and stepped care. After identifying a PMI, a care pathway needs to be defined, including diagnostic and coordinated needs-based care. For severe PMI, specialized services should be available and provided by trained psychiatrists and specialists, such as perinatal mental health midwives. In Austria, various services for different severity levels of PMI are available. However, large geographical variations and gaps exist regarding specialist services, such as mother–baby units. Specially trained health professionals are lacking. Still, almost one in five mothers claimed one of the defined mental health benefits during the perinatal period.

PIMHC in Austria currently does not follow best-practice recommendations. The main gaps are in providing guideline-based specialized care by trained professionals. Insurance-funded mental health service use during the perinatal period corresponds with international prevalence figures, although only a part of available services is included in the analysis. To ensure women receive high-quality care, PIMHC needs to be more prioritized.

Several drugs are licensed for the treatment of relapsing-remitting multiple sclerosis (RRMS) in Brazil. The technological horizon of the disease introduces some additional therapies not yet approved in the country. The objective of this study was to establish an efficiency frontier for medicines used for RRMS, to guide the entry of new therapies into the market.

Primary data on the annualized relapse rate (ARR) and sustained disability progression for six months (CDP6), from 15 different therapies approved in Brazil, were obtained from the literature. Two network meta-analyses (NMA) were conducted comparing multiple treatments based on ARR and CDP6. A Markov model with transition states based on the Expanded Disability Status Scale (EDSS), with an annual cycle and a lifetime time horizon, was built to carry out the cost–utility assessment with an efficiency frontier.

NMA results for ARR and CDP6 were used as inputs for the economic model. In the cost–utility analysis, all strategies were dominated by the lowest-cost medication (teriflunomide) and the most effective one (alemtuzumab). In the deterministic sensitivity analysis, alemtuzumab was the dominant drug in 78 percent of simulations. In probabilistic analysis, most medications had an incremental cost-effectiveness ratio (ICER) below the Brazilian cost-effectiveness threshold in over 90 percent of simulations (approximately USD8,000/quality-adjusted life years [QALY]). An efficiency frontier was established between teriflunomide and alemtuzumab, with an ICER of USD11,141/QALY.

RRMS presents a scenario where several therapies compete for the Brazilian market, but only two of these drugs remained within the established efficiency frontier: alemtuzumab and teriflunomide. For the sustainability of public and private health systems, the efficiency frontier could serve as a cost-effectiveness benchmark, requiring new therapies considering entering the market to adapt to the set parameters.

Innovative designs have been used to enhance trial efficiency and enable access to novel treatments when traditional randomized controlled trials (RCTs) were considered operationally challenging to implement. Besides demonstrating scientific and regulatory rigor, innovative approaches also need to be perceived favorably by payers. This work delves into payer perspectives on innovative designs, identifying circumstances that contribute to higher payer acceptability.

Using targeted searches, we mapped clinical trials since 2010 with novel design elements, including adaptive, master protocol, hybrid, enrichment, and innovative endpoints. Sixty-two asset-indication examples using these designs were identified across different therapeutic areas. Based on the availability of health technology assessment (HTA) reports and the innovative element’s impact on final HTA outcome, 17 of these identified examples were developed as case studies to highlight the designs’ implications for access. Interviews with eight payer-experts across US, France, Germany, and Japan were conducted to further validate the research, explore scenario analyses, and clarify how circumstances impacted payer acceptability of the studied innovative trial designs.

While oncology has historically spearheaded innovative trial designs, other therapeutic areas are now incorporating innovative elements. Nonetheless, published payer guidance on innovative designs remains limited. We identified seven circumstances impacting payer acceptance of innovative designs: disease prognosis, eligible patient population size, type of treatment, mode of action, availability of treatment alternatives, comparative benefit versus standard of care, and launch sequence. Patient population size had the greatest impact on payer decisions, followed by comparative benefit and type of treatment; this suggests that payers may be inclined to accept innovative trial designs for small populations with high unmet need or therapies with transformative clinical benefits.

Innovative trials trade some scientific validity for increased practicality compared to traditional RCTs. Stakeholders need to align on appropriateness and scientific validity of innovative designs for access decisions, while implementing measures to minimize uncertainties. Collaboration across stakeholders including regulators, payers, and manufacturers is needed to refine innovative methodologies and guide policies to improve the relevance of innovative trials for decision-making.

There are concerns with the National Institute for Health and Care Excellence (NICE) conditional approval process with respect to the quality and reliability of the methods of evidence generation, analysis, and reporting; and on the increasing focus on OwR (only with research) in preference to OiR (only in research). Our study critically appraises the methods, quality, and risk of bias of the evidence generated in response to NICE conditionally approved technologies.

Our study reviewed pharmaceutical technologies appraised by NICE: technology appraisals approved between March 2000 and September 2022 and highly specialized technologies approved up to October 2023. From those, we identified appraisals with OiR and OwR conditional recommendations, and summarized the evidence requested by NICE as part of conditional approval. Then, evidence resubmitted to NICE for reappraisal was analyzed for its compliance with NICE’s initial recommendations for further research and assessed using the Cochrane Collaboration’s tools for risk of bias in randomized trials and the ROBINS-I tool for the non-randomized evidence.

NICE made 54 conditional recommendations relating to technology appraisals (TAs) (13 OiR and 41 OwR) and five highly specialized technologies (HSTs). From those, 16 TAs presented additional evidence for reappraisal [nine OiR and seven OwR] and three HSTs [three OwR]. Two of the nine reappraised TAs with OiR recommendation and four of the seven OwR fully complied with NICE’s request for further evidence. All three HSTs complied in full. However, the majority of reappraised TAs and HSTs included evidence that was deemed to be at serious, high, moderate, or unclear risk of bias.

The quality of evidence presented to NICE following conditional approval varies considerably. There is often widespread noncompliance with requests for further research when conditionally approving a pharmaceutical technology. Complying with NICE’s requests, however, does not necessarily guarantee high-quality evidence when the technology is reappraised. Evidence generated in response to NICE conditional approval recommendations should be subject to quality standards.