High-cost gene therapies strain the sustainability of healthcare budgets. Despite the potential long-term savings promised by certain gene therapies, realizing these savings faces challenges due to uncertainties regarding the treatment’s durability and a lesser-discussed factor: the true potential for cost offset. Our study aims to assess the cost-offset uncertainty for US Medicaid regarding recently approved gene therapies in hemophilia A and B.

The analysis used 2018 to 2022 Colorado Department of Health Care Policy & Financing data to determine direct costs of standard of care (factor replacement therapy or emicizumab). Cost-simulation models over five- and ten-year time horizons estimated Colorado Medicaid costs if patients switched to gene therapy (valoctocogene roxaparvovec or etranacogene dezaparvovec) versus maintaining standard of care. Patients were included if aged 18 and over with ICD-10-CM codes D66 (hemophilia A) and D67 (hemophilia B). In the base case, severe hemophilia A was defined as requiring greater than or equal to six yearly factor VIII or emicizumab claims and moderate/severe hemophilia B requiring greater than or equal to four factor IX replacement therapy claims annually.

Annual standard-of-care costs were USD426,000 (SD USD353,000) for hemophilia A and USD546,000 (SD USD542,000) for hemophilia B. Valoctocogene roxaparvovec (hemophilia A) had incremental costs of USD880,000 at five years and −USD481,000 at 10 years. Sensitivity analysis revealed a 23 percent chance of break-even within five years and 48 percent within 10 years. Etranacogene dezaparvovec (hemophilia B) showed incremental costs of USD429,000 at five years and −USD2,490,000 at 10 years. Simulation indicated a 32 percent chance of break-even within five years and 59 percent within 10 years. Varying eligibility (≥4 to ≥15 standard-of-care claims) notably affected break-even; for example, valoctocogene roxaparvovec: 40 percent to 77 percent chance of break-even in 10 years.

Our study highlights significant cost variation in the standard of care of patients eligible for gene therapies, adding to the uncertainty surrounding cost estimation and highlighting the importance of addressing this factor in risk-sharing agreements. The impact of varying eligibility criteria on cost offsets emphasizes the importance of carefully defining eligibility when using real-world data in the context of health technology assessment.

Patients with amputation need a safe and comfortable connection with the prothesis. Traditional sockets may lead to skin tearing, pain, and limitation of movement. Osseointegrated protheses connected to residual bone may have a positive impact on patients’ quality of life. Our research question: Are there economic and organizational benefits from the introduction of a direct skeletal attachment (DSA) procedure, as well as clinical benefits?

Firstly, a systematic literature review was carried out on PubMed and Scopus, and the Critical Appraisal Skills Programme (CASP) tool was applied to assess the evidence’s quality. A health technology assessment (HTA) evaluation was conducted (EUnetHTA, 2016), adopting the perspective of a public hospital in Italy, comparing two scenarios—traditional and innovative—related to traditional protheses and the DSA approach. Seven experts (orthopedic surgery, physical medicine, and rehabilitation and physiotherapy) were involved for the administration of a qualitative questionnaire. For the economic evaluation, a cost-effectiveness analysis and budget impact analysis were defined. Finally, a multiple-criteria decision analysis was performed.

The literature search yielded 314 citations published until December 2022: eight were eligible for analysis. Three were the system analysed: ‘OPRA’, ‘ISP’ and ‘OPL’. The efficacy of the systems is linked to a better distribution of bone stress: an increase in bone mineral density was recorded near the implants (respectively 28%, 27%, and 18% – after 60 months). The safety of DSA depends on the design and integrity of the connection between tissues and implant. The impact on the budget is an increase of 27 percent in costs for each patient treated. Concerning the social and ethical implications, DSA results in the preferable approach (1.48 vs 0.34), as it can limit social costs (0.29 vs −0.29).

The comparative evaluation was carried out using a scoring method: the main advantages related to innovative prostheses are based on effectiveness and safety, as well as social impact and organizational impact, especially due to the ability of the prostheses to reduce the risk of adverse events and long rehabilitation, with important clinical benefits and organizational savings for the hospital management.

Healthcare regionalization is the movement of responsibility of health care towards a regional body. It has been introduced in many countries and exists in many forms across high-, middle- and low-income countries. Supporters of regionalization purport that a more actively managed system, with better coordination and integration, could lead to improved quality and patient-centered care. However, evidence for this is unclear.

Systematic searches combining terms for regionalization and patient-centered care in MEDLINE identified 5,765 titles for review. Three levels of screening were conducted by two independent reviewers: title only, abstract and title, and full-paper review. Rapid-realist synthesis methodology was used to gather a deeper understanding of the relationship between healthcare regionalization and patient-centered care, seeking to identify potential mechanisms and the context in which these operate. We also sought to determine whether novel methodologies such as this can be used by health technology assessment (HTA) bodies in an efficient manner that produces results directly applicable to decision-makers.

Studies from high-income countries, including Canada, New Zealand, Australia, and Italy, were included. The realist synthesis identified mechanisms by which whole healthcare-system regionalization can help or hinder the rollout of patient-centered care. Mechanisms were classified in relation to specific dimensions of patient-centered care including access and “patient as person.” Facilitators to the use of rapid-realist review in health policy include similarity of screening, searching, and extraction to traditional systematic review. Barriers include the scope of the literature considered relevant, length of time to familiarize with the method, and presentation of the findings in an accessible way for policymakers.

This is the first realist synthesis of the relationship between whole healthcare-system regionalization and patient-centered care. Regionalization may help or hinder achievement of patient-centered care. Policymakers should note barriers to, and facilitators of, patient-centered care in the context of large-scale health system reform. Rapid-realist review has applications for HTA, particularly in the exploration of non-standard interventions.

In the era of cardiovascular-kidney-metabolic syndrome, thorough evaluation of medicines with multiple treatment effects/indications demands a multifaceted modeling philosophy, despite the requirement of health technology assessment (HTA) models to focus on one disease. Using Cardiff, a model previously built for type 2 diabetes (T2D), we illustrate the changes needed to capture contemporary, holistic, patient-centered decision-making, and argue that HTA bodies should revise their approach.

The upgraded model enables therapy selection and escalation determined by HbA1c thresholds, cardiovascular risk (QRISK3), comorbidities (established cardiovascular or chronic kidney disease), and weight (body mass index ≥35 kg/m2). Risk factor trajectories were updated by incorporating UKPDS-90 equations and other relevant data sources. Clinical outcomes were predicted using new risk equations incorporating cardiovascular outcomes trial data whenever possible. The updated model was applied to assess quality-adjusted life years (QALYs) and lifetime costs in newly diagnosed T2D patients in the UK, modeled via a conventional glycemic-centric approach versus a multifactorial treatment algorithm. Extrapolation to the national level utilized estimates of annual incidence.

The updated treatment algorithm captured and quantified the impact of nuanced comorbidity management called for in guidelines. In a cohort of newly diagnosed T2D patients, 81 percent initiated an SGLT2 inhibitor within five years, predominantly due to increasing cardiovascular risk, versus zero percent when escalation was dictated by HbA1c alone. Broad, early use of SGLT2 inhibitors resulted in an additional 0.73 predicted QALYs and GBP10,757 (USD13,600) in predicted lifetime cost savings per patient versus a “traditional” approach. Cost savings were primarily due to avoided renal events; extrapolation to the national level predicted cost savings to the payer of GBP2.8 billion (USD3.5 billion), which traditional models cannot capture.

The modernized Cardiff model incorporates multifactorial prescribing guidelines and contemporary evidence around cardio-renal protection and is more adept at modeling costs and outcomes of multidimensional antidiabetic treatments; traditional glucose-centric modeling methods may introduce bias. Economic modeling and HTA processes must adapt to follow the complexities of modern disease management and remain relevant as healthcare systems address the cardiovascular-kidney-metabolic syndrome epidemic.

Multiple myeloma (MM) is a challenging hematological malignancy, primarily treated with autologous stem cell transplantation (ASCT). However, relapse or refractoriness is inevitable, necessitating alternative treatments. This study evaluates ciltacabtagene autoleucel (Carvykti®), a novel therapy, against a second ASCT, using an efficiency frontier approach to assess its therapeutic value and cost-effectiveness.

We conducted a comparative analysis using data from CARTITUDE-1 clinical trials and a Brazilian real-world cohort (2002 to 2015) of MM patients treated under SUS (Brazilian Healthcare System). We estimated survival curves and area under the curve (AUC) for both interventions over 48 months and projected the curves for a 10-year horizon using parametric distributions. Cost-effectiveness was assessed by calculating the incremental cost per month of survival. Efficiency frontier methodology was employed to determine a proportional price for ciltacabtagene autoleucel, based on the cost and median survival benefits compared to the second ASCT.

Ciltacabtagene autoleucel demonstrated a 7.27 percent increase in AUC for overall survival over 48 months compared to the second ASCT. The incremental cost was BRL54,219.15 (USD11,133.30) per month of survival. Over a 10-year horizon, the estimated cost for ciltacabtagene autoleucel was significantly higher than that for the second ASCT. Using the efficiency frontier approach, the cost of ciltacabtagene autoleucel should not exceed BRL228,226.42 (USD46,863.74), considering its survival benefit and cost of production.

Ciltacabtagene autoleucel demonstrates significant anti-tumor activity in relapsed/refractory MM, with a notable survival advantage. Efficiency frontier analysis suggests a maximum justified cost, providing a framework for pricing decisions. This study highlights the importance of balancing innovation with cost-effectiveness in healthcare decision-making.

Increasingly in Ireland, there are specific criteria attached to reimbursement approval for new medicines. Health technology assessment (HTA) identifies where uncertainty is greatest in relation to clinical and cost-effectiveness evidence and budget impact estimates; our health technology management (HTM) approach uses these outputs from HTA to design protocols to manage these uncertainties in the post-reimbursement phase.

A bespoke managed access protocol (MAP) is developed for each medicine reimbursed under this approach, informed by uncertainties highlighted in the HTA, directions from the decision-maker, and relevant particulars arising from commercial negotiations. Individual patient reimbursement applications are submitted via an online application system linked directly to the national pharmacy claims system. Pharmacists review the applications and approve reimbursement support where the patient meets the reimbursement criteria. The process is adaptive, allowing expansion of the criteria to include previously excluded patient cohorts, and the addition of new indications. It can also work across differing reimbursement arrangements (hospital/primary care).

The MAP for liraglutide for weight management confines reimbursement to patients with a body mass index greater than or equal to 35 kg/m², prediabetes, and high risk for cardiovascular disease. Phase I reimbursement support lasts for six months; patients not attaining greater than or equal to five percent weight loss are deemed non-responders as per the HTA, and reimbursement support is discontinued. The MAP for dupilumab confined reimbursement support to adults with refractory moderate-to-severe atopic dermatitis, where cost-effectiveness was plausible in the HTA. The MAP for calcitonin-gene-related-peptides monoclonal antibodies confines reimbursement support to patients with chronic migraine, refractory to at least three prophylactic treatments, where cost-effectiveness was plausible in the HTA.

Across these MAPs, over 3,000 patients accessed novel treatments for chronic illnesses in September 2023. HTM provides an effective mechanism to facilitate access to high-cost medicines for targeted patient groups, while providing increased oversight and budgetary certainty. Key to acceptance is utilization of HTA outputs to implement evidence-based HTM measures targeting specific uncertainties as highlighted in the HTA report.

In the last decade, artificial intelligence (AI) has been increasingly applied in health technology assessment (HTA) to accelerate evidence synthesis, optimize resources allocation, and guarantee timely delivery of trustworthy technologies in health. The aim of the present scoping review is to map AI models applied in HTA, and technical characteristics of AI-based automation and semi-automation applied in HTA.

A search strategy containing core expressions “AI” and “HTA” and correlated terms was conducted in nine specialized databases (health and informatics) in February 2022. Inclusion criteria were publications testing AI models applied in HTA. Study selection was performed by independent pairs, with consensus meetings. No filters were applied. Data on year and country of publication, HTA phase, subsets of AI (e.g., machine learning [ML], neural networks), type of algorithm (e.g., support vector machine [SVM], K-nearest neighbors), and performance scale were extracted. Data were analyzed as descriptive frequency statistics. Used metrics will be presented narratively.

Sixty-one publications were included. The first study identified was published in 2006, and since then the number of publications has been consistently growing, with 11 publications in the year 2021. Canada, USA, and the UK concentrate 72 percent of publications (44 in 61) equally distributed. The most common HTA phase was the evidence synthesis, with 59 studies (96%). The main task performed was study screening/selection (66.6%). The majority of ML models (80.9%) contained two learning nodes or fewer, and applied SVM and decision-tree-based algorithms. Inter-rater agreement, accuracy, and 95 percent recall were the most common scales observed.

Although recent developments in AI applied to HTA show increasing potentiality, studies are concentrated in the study selection phase of evidence synthesis. Many areas need further development, such as horizon scanning and policymaking processes. Additionally, studies reporting time gain and economic gain outcomes are scarce and should be considered for the development of future studies in the field.

Artificial intelligence (AI) technologies are becoming more widespread in daily life, including in health and care settings. AI is being developed for use in many areas, including diagnostics, treatment planning, and patient monitoring. There are new challenges and additional considerations when performing a health technology assessment (HTA) for AI use in healthcare.

Health Technology Wales has developed additional guidance and a checklist of considerations for researchers to use when undertaking an HTA on AI. These documents were created as a result of involvement in discussions with healthcare services around AI; attendance at meetings and training sessions with government, healthcare, and academic institutions; and observations from undertaking our first HTAs investigating AI technologies.

We identified seven main areas to consider: (i) the AI model being used and its training datasets; (ii) clarity on where in the clinical pathway the AI sits and whether this is appropriate for the local healthcare service; (iii) clear identification of the target patient population and whether there are gaps in the evidence base for subgroups; (iv) who uses the AI and the training required; (v) possible barriers and inequities in usage, particularly if the AI is patient facing; (vi) ongoing monitoring of the AI model; and (vii) data security and usability.

HTA assessment of AI presents new challenges for HTA bodies. Additional outcomes for assessment (including user acceptability) may be required, and an awareness of background information that allows for contextualization of the technology and clear indication of its usefulness in the local healthcare setting is key. The development of guidance related to AI technologies may enable rigorous evaluation.

Innovative pricing and payment schemes have been proposed to address the affordability issues raised by new health technologies or the uncertainty about their long-term safety and effectiveness. As part of the Horizon Europe project HI-PRIX, we investigated the nature, scope, and impact of these arrangements.

We undertook a PRISMA-ScR-compliant review in PubMed, Web of Science, and Scopus, from 2010 to 2023. We also searched health technology assessment (HTA) agency websites. The search strategy was structured around two blocks: “pricing/payment schemes,” and “innovativeness.” Studies illustrating pricing/payment schemes with sufficient level of details to explain their functioning were selected, also through a nested evaluation of an artificial-intelligence-powered tool for systematic reviews. These schemes were classified according to several criteria, such as their purpose, nature, governance, product category, data collection needs, foreseen distribution of risk, and implementation challenges. The study protocol was published in PROSPERO (CRD42023444824).

“Innovative payment and pricing schemes” were defined as arrangements that go beyond price per unit of the technology, simple price/volume agreements, or expenditure caps. Seventy innovative schemes were identified, of which 25 were only illustrated theoretically, while 45 have been implemented in practice. So far, 170 real cases of implementation have been identified. The schemes target pharmaceuticals, vaccines, and/or medical devices. Whether designed to incorporate unique features of a given technology, or to address specific challenges, the schemes can be classified by different value drivers, including type of technology, therapeutic indication, or timeline of the agreement.

Available pricing and payment schemes have the potential to offer a comprehensive toolkit to policymakers facing reimbursement and access decisions, highlighting that it is not the scheme per se that is innovative, rather its application/use in a given context or for a given challenge. The catalog populates the Pay for Innovation Observatory, a publicly available repository.

Effective health intervention coverage decision-making hinges on understanding budget impact (BI). Despite progress in estimating cost-effectiveness thresholds, a standardized approach for defining BI, particularly high BI, remains elusive. Addressing this gap, our systematic review aims to identify existing BI thresholds and establish universally applicable BI categories, providing a much-needed framework for global health policy.

In our systematic review, we adhered to Cochrane methods and PRISMA reporting guidelines (PROSPERO protocol CRD42020221652). We included articles that detailed current BI or affordability thresholds used by national or regional healthcare systems, sourcing from PubMed, Embase, and International Network of Agencies for Health Technology Assessment (INAHTA) communications. To address variability across jurisdictions, we normalized BI/affordability thresholds to a fraction of each country’s total healthcare expenditure. This approach enabled us to categorize BI thresholds into four distinct levels (low, moderate, high, and very high) and apply these categories universally across countries.

We retrieved 1,592 records, identifying affordability thresholds and their underlying rationales in 12 countries: Argentina, Australia, England, Canada, Germany, France, Netherlands, USA, Taiwan, Ukraine, Scotland, and Singapore. Utilizing this data, we established four BI threshold levels relative to the total health budget: low (below 0.00005), moderate (0.00005 to <0.0001), high (0.0001 to <0.0002), and very high (>=0.0002). We then extrapolated these thresholds, along with their uncertainty ranges, to 174 countries, using 2022 World Bank data.

Our study provides a comprehensive overview of current global affordability thresholds and their implications for healthcare coverage and reimbursement. We found that explicit BI thresholds are predominantly established in high-income countries. Our findings offer critical, evidence-based guidance on affordability decision rules, applicable to health systems in 174 countries, thereby contributing significantly to the standardization and informed policymaking in global healthcare.

The healthcare ecosystem is predominantly supply-driven, leading to a lack of innovation in financially less-attractive health areas, inefficient use of public resources for healthcare, and unmet patient and societal needs. This study proposes a framework to identify unmet health-related patient and societal needs for different health conditions, to inform and support the development of needs-driven healthcare policy and innovations.

Two systematic literature reviews were conducted: one to update an already published literature review that identified tools for measuring patient needs; another to identify criteria for measuring health-related societal needs. Ovid MEDLINE and Embase were searched for English, French, and Dutch publications. In April 2023, 22 Belgian stakeholders and experts reviewed selected societal needs criteria. The NEED (Needs Examination, Evaluation and Dissemination) assessment framework, incorporating literature-derived patient and societal needs criteria, underwent discussions with the Health Minister’s Cabinet and federal health agencies from May to July 2023. The framework was challenged in November 2023 during international and national advisory committee meetings.

The literature reviews covered 52 studies. The NEED framework addresses patient, societal, and future needs across health, healthcare, and social domains. Patient-level criteria (13) include health (e.g., impact on quality of life), healthcare (e.g., burden of treatment), and social (e.g., social support). Societal needs criteria (6) encompass health (e.g., transmissibility), healthcare (e.g., value for money), and social (e.g., productivity losses). Future needs criteria (2) consider future burden of disease and economic burden. Each criterion (total: 40) has measurable indicators with calculation methods and data sources. Equity is recognized as a transversal dimension, requiring unmet needs data disaggregated by population group.

For the first time, a transparent scientific framework containing criteria and indicators for assessing the unmet needs of patients and society has been developed. This lays the foundations for a major shift towards a more needs-driven healthcare policy and innovation.

The NEED (Needs Examination, Evaluation and Dissemination) assessment framework aims to identify patient and societal unmet needs in various diseases, highlighting gaps for intervention. This approach is exemplified through a malignant melanoma case study, ranking sixth in European cancers. Therapeutic progress has been made over the last decade, prompting an assessment of remaining unmet needs.

The applicability of the NEED framework has been evaluated through a case study of melanoma in Belgium. The methodology had four components: (i) an overall description of the disease based on the scientific (published and grey) literature and clinical expert input; (ii) an online survey on the unmet needs of patients (n=37); (iii) semistructured interviews with individuals affected by melanoma (n=9); and (iv) secondary data collection for each NEED framework criterion, including scientific literature and public databases. The survey results were analyzed with descriptive statistics, and the interview transcripts were analyzed using a thematic analysis.

Unmet needs in melanoma were categorized as patient-level health, healthcare, and social needs, alongside wider societal needs. Patients experienced a substantial burden from mortality in advanced stages, burdensome symptoms such as fatigue, and notable side effects of systemic treatment. Inadequate psychological support contributed to high levels of fear and anxiety. A scarcity of dermatologists posed a barrier to timely care access, and low awareness of melanoma risk led many patients to delay seeking medical attention, with significant societal implications for melanoma prevention efforts. The case study also identified research gaps concerning social and ethnic inequalities in melanoma care in Europe.

Using the NEED framework highlighted melanoma patients’ most crucial unmet needs. This evidence can be used to assess whether new interventions address the highest unmet patient or societal needs. Challenges in ensuring adequate sample size limit the generalizability of results. Capturing melanoma’s complexity in a snapshot masks some needs heterogeneity.

Celiac disease (CD), an autoimmune disorder triggered by gluten, impacts about one percent of the population. Only one-third receive a diagnosis, leaving the majority unaware of their condition. Untreated CD can lead to gut lining damage, resulting in malnutrition, anemia, and osteoporosis. Our primary goal was to identify at-risk groups and assess the cost-effectiveness of active case finding in primary care.

Our methodology involved systematic reviews and meta-analyses focusing on the accuracy of CD risk factors (chronic conditions and symptoms) and diagnostic tests (serological and genetic). Prediction models, based on identified risk factors, were developed for identifying individuals who would benefit from CD testing in routine primary care. Additionally, an online survey gauged individuals’ preferences regarding diagnostic certainty before initiating a gluten-free diet. This information informed the development of economic models evaluating the cost-effectiveness of various active case finding strategies.

Individuals with dermatitis herpetiformis, a family history of CD, migraine, anemia, type 1 diabetes, osteoporosis, or chronic liver disease showed one and a half to two times higher risk of having CD. IgA tTG, and EMA demonstrated good diagnostic accuracy. Genetic tests showed high sensitivity but low specificity. Survey results indicated substantial variation in preference for certainty from a blood test before initiating a gluten-free diet. Cost-effectiveness analyses showed that, in adults, IgA tTG at a one percent pre-test probability (equivalent to population screening) was the most cost effective. For non-population screening strategies, IgA EMA plus HLA was most cost effective. There was substantial uncertainty in economic model results.

While population-based screening with IgA tTG appears the most cost effective in adults, decisions for implementation should not solely rely on economic analyses. Future research should explore whether population-based CD screening aligns with UK National Screening Committee criteria and requires a long-term randomized controlled trial of screening strategies.

Approximately 42,000 new cases of colorectal cancer (CRC) are diagnosed annually in the United Kingdom with 16,800 deaths. Evidence suggests that quantitative fecal immunochemical tests (FIT) are a good predictor of CRC risk in symptomatic patients presenting to primary care. We aimed to assess the cost-effectiveness of FIT in this setting, considering capacity constraints and waiting times for subsequent colonoscopy.

We compared two diagnostic FIT strategies, at various thresholds, in the model: (i) FIT for all patients and (ii) current practice where only low-risk patients received FIT. Patients with positive FIT scores and high-risk patients in current practice received colonoscopy. Diagnostic accuracy evidence from published literature, standard UK cost sources, and other sources were used to estimate health outcomes and costs. Waiting times before colonoscopy were assumed proportional to the numbers referred, with the impact of delayed colonoscopy taken from published models. Savings per quality-adjusted life years (QALYs) lost and incremental net monetary benefit (INMB) were used. Uncertainty was evaluated.

Model results suggested that, compared to current practice, FIT generated a positive INMB for the majority of thresholds assessed (GBP200 [USD254] to GBP350 [USD445] per patient at a willingness to pay of GBP20,000 [USD25,474] per QALY gained). A reduction in the number of patients sent to colonoscopy led to cost savings. However, these thresholds were associated with slight QALY losses due to a small proportion of false negative results associated with significantly delayed diagnosis, which outweighed the benefits associated with quicker times to colonoscopy for those with positive FIT results. Savings of over GBP100,000 (USD127,374) per QALY lost were generated. Conclusions were robust to the sensitivity analyses undertaken.

With capacity constraints explicitly represented in the economic modeling, offering FIT to all patients presenting to primary care with symptoms suggestive of CRC was cost effective when compared to current practice. However, the optimal threshold could not be robustly determined due to limited diagnostic accuracy data, parameter uncertainty, and limitations in the model structure; additional primary research could reduce uncertainty.

Environmental sustainability and its incorporation in health technology assessment (HTA) is becoming increasingly researched globally. However, this has yet to lead to a significant impact on HTA processes. This research presents a novel case study, demonstrating how available methods can deepen the understanding of evidence-based approaches to healthcare decision-making and support HTA sustainability policy development.

A decision-analytic model was developed for a digital health technology used to support primary care in the diagnosis and triage of musculoskeletal conditions. The model mapped the potential impact on the care pathway, capturing differences in resource use, including appointments, medications, diagnostic tests, surgical procedures, and other nonpharmacological treatments. The model was populated from a UK perspective and captured both health economic impact and carbon dioxide equivalents (CO2e) impact. Additional potential environmental impacts were then considered qualitatively as part of the evaluation.

The health economic modeling approach captures all stages of the patient care pathway and resource use demonstrating its practicality for simultaneously mapping out the carbon impacts. This methodological approach is reproducible, transparent, and provides a standardized tool for use in future carbon-cost-comparison modeling. This would present decision-makers with more complete information. There are some limitations to this approach, such as ambiguity regarding some carbon data estimates used, but it still provides a more useful summary than no estimated quantification.

Adapting HTA will support wider efforts in health systems to reduce environmental impacts. This model can be practically applied to account for both cost and carbon data, facilitating a holistic and environmentally sustainable approach to decision-making. As part of encouraging additional research into the environment, HTA agencies will need to provide “incentives” for companies to undertake this additional research.

The environmental footprint of the healthcare sector is widely acknowledged and increasingly impacting decision-making in health policy. This study aims to explore what methods and metrics have been proposed to measure environmental impact of health technologies, and to investigate the feasibility and consequences of its inclusion in economic evaluations. This study is part of a larger Horizon Europe project, HI-PRIX.

We conducted a PRISMA-ScR-guided scoping review of the scientific publications in PubMed, Web of Science, and Scopus between 2013 and November 2023. For the grey literature, the international HTA database and HTA agencies websites were searched to identify ongoing or published HTA dossiers. The search strategy was developed around two concepts: environment and health technology assessment. Any study or report that described a conceptual framework, methodology, or approach used or theorized to integrate environmental impacts of health technologies in an HTA was considered eligible for inclusion.

Starting from 12,336 records, 16 scientific publications and 6 HTA dossiers were included for analysis. One-third of the contributions were published in 2023 and were mostly opinions, editorials, reviews, or economic evaluations. Lifecycle assessment was considered the preferred approach to investigate environmental impacts, yet difficulties exist; for example, methodologies for measuring greenhouse gas emissions are established, while for other dimensions are under development. Nevertheless, methods to incorporate environmental impact into economic models appear viable. About half of the papers analyzed cost–utility analysis, multiple-criteria decision analysis, and cost–benefit analysis as potential approaches. Fewer papers addressed budget impact, cost-effectiveness analysis, or other approaches.

Incorporating the environmental impact of health technologies in economic evaluations is under development, but consensus is still lacking on appropriate, feasible methodologies for its uptake. Before considering it in pricing or reimbursement-related decisions, the preferred method to maximize utility levels of different stakeholders (e.g., HTA agencies, industry) needs clarification.

Environmental impact has been poorly addressed in health technology assessment (HTA) processes despite its potential role in promoting more sustainable health systems. Initiatives to incorporate this dimension into economic evaluations (EE) that support HTA are few and far between. We aim to identify the state of the art and challenges for incorporating environmental impact into the EE of HTA.

We conducted a scoping review to identify publications on the assessment of the environmental dimension of health technologies from different approaches: establishment of theoretical frameworks and methods; data search strategies; identification of parameters, designs, and indicators; as well as descriptions of practical applications in HTA (literature review, EE, or budget impact analysis). The literature search was conducted through PubMed. Selected studies should provide insights to incorporate environmental impact into the EE of HTA regardless of the technology or environmental aspect considered (carbon footprint, use of resources, waste generation, etc.).

From a total of 219 references initially identified, 22 publications meeting the selection criteria were found. The holistic approach is recognized as the most appropriate for incorporating the environmental dimension, through the evaluation of the entire life cycle of the technology, as well as the management of the disease and the use of resources throughout the care process. A large amount of information and accurate estimates about the impact of the technology are needed. Therefore, the first reported approaches have focused on particular aspects of the environmental impact of a health technology (mainly the carbon footprint).

The practical incorporation of the environmental dimension into the HTA is still very incipient. Foundations have begun to be established for its incorporation into economic evaluation. A consensus is required on the most appropriate methodologies and tools to collect the necessary data. It would also require a multidisciplinary approach and a framework for cooperation between all the stakeholders.

Technical and trade-off issues to incorporating environmental data in health technology assessment (HTA) decision processes are known. Multiple-criteria decision analysis (MCDA) has been successfully used across disciplines, supporting multifaceted technology decision-making. Challenges in prioritizing environmental criteria, resource constraints, and other criteria to assess technologies can be overcome through the application of MCDA, also used in conjunction with other techniques in HTA.

A systematic review of methods to evaluate environmental sustainability in HTA was conducted following the PRISMA guidelines. Using a comprehensive search strategy, Ovid Embase, Web of Science (all databases), PubMed, EBSCOhost GreenFILE, IEEE Xplore, international HTA database, Cochrane Library, and grey literature were searched. The review analyzed the following broad themes: methods to overcome barriers, including how methods can handle “trade-off” issues between financial and environmental considerations; resource and expertise constraints; and data quality issues. This review determined how comprehensive the methods are for assessing sustainability in HTA. Frameworks were also ranked based on their overall transparency and feasibility.

This review (SR) identified 10 key studies. Half of these studies outline MCDA models within the frameworks. All MCDA studies have been combined with other techniques to support sustainable technology decision-making HTA. Analytical MCDA models with roots in mathematics are highlighted as reproducible techniques to support multifaceted decision-making, particularly where there are conflicting criteria. Analytical hierarchy process (AHP) used in conjunction with a circular economy framework for health technology supports global health system net zero and wider sustainability goals. A performance matrix model elicits outcome trade-off by assigning weights to costs, technical performance, and environmental outcomes.

The multidisciplinary findings of this research provide valuable insight in an area where there is currently limited research, confirming and expanding on previous scoping reviews. The results highlight several comprehensive MCDA models supporting HTA sustainability framework development. Further research is warranted into the feasibility of the frameworks. Study selection and data extraction were undertaken independently due to project time constraints.

Cost-effectiveness modeling often requires extrapolation of survival data from clinical trials over a long-term horizon. The choice of extrapolation method is often uncertain and can have a profound impact on the results. We propose a novel Bayesian approach towards incorporating external information (e.g., registry data or clinical opinion) into the extrapolation process as a means of reducing this uncertainty.

Standard parametric survival curves are fitted to immature time-to-event data using maximum likelihood estimation (MLE). Separately, external information on expected cohort-level survival at a future time point is used to specify a prior probability distribution. These are combined to generate posterior distributions of survival curve extrapolations that simultaneously incorporate both observed data and external information. This is done using importance sampling and multivariate normal approximations of the likelihood and posterior distributions; it requires only summary model parameter estimates (and not patient-level data). We apply our method to analyze survival data from the KEYNOTE-426 trial of pembrolizumab+axitinib in advanced renal cell carcinoma.

The method was implemented in R, and outputs survival curve parameters that are compatible with cost-effectiveness models developed in other software (e.g., Microsoft Excel). In all examples considered, our method resulted in extrapolated survival predictions that were more closely aligned with the external information compared with the standard (MLE-based) approach. Incorporation of external information decreased between-distribution variance (reduced structural uncertainty), and generally also decreased within-distribution variance as well (reduced parameter uncertainty). Results were comparable with those obtained from the method of Cooney and White, which uses similar ideas but requires full patient-level data and is more computationally complex.

The extrapolation method we describe can reduce uncertainty when valid external information is available. Only MLE-based parameter estimates are required to implement our method, thus secondary model users such as HTA agencies can adjust survival extrapolations from existing cost-effectiveness models without access to patient-level data. Implementation is straightforward and computationally efficient, and outputs are easily incorporated into existing cost-effectiveness models.