The development and strengthening of health technology assessment (HTA) capacity on the individual and organizational level and the wider environment is relevant for cooperation on HTAs. Based on the Maltese case, we provide a blueprint for building HTA capacity.

A set of activities were developed based on Pichler et al.’s framework and the starting HTA capacity in Malta. Individual level activities focused on strengthening epidemiological and health economic skills through online and in-person training. On the organizational level, a new HTA framework was developed which was subsequently utilized in a shadow assessment. Awareness campaign activities raised awareness and support in the wider environment where HTAs are conducted and utilized.

The time needed to build HTA capacity exceeded the planned two years accommodating the learning progress of the assessors. In addition to the planned trainings, webinars supplemented the online courses, allowing for more knowledge exchange. The advanced online course was extended over time to facilitate learning next to the assessors’ daily tasks. Training sessions were added to implement the new economic evaluation framework, which was utilized in a second shadow assessment. Awareness by decision-makers was achieved with reports, posters, and an article on the current and developing HTA capacity.

It takes time and much (hands-on) training to build skills for conducting complex assessment such as HTAs. Facilitating exchange with knowledgeable parties is crucial for succeeding as well as the buy-in of local managers motivating staff. Decision-makers need to be on-boarded for the continued success of HTA capacity building.

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.

To achieve universal health coverage (UHC), countries must make difficult choices to optimize the use of scarce resources. There is a growing interest in using evidence-based priority setting processes, such as Health Technology Assessment (HTA), to inform these decisions. In 2020, the Palestinian Institute of Public Health (PNIPH) and the Norwegian Institute of Public Health (NIPH) initiated a pilot to test the feasibility of coproducing an HTA on breast cancer screening in the West Bank, occupied Palestinian Territory. Additionally, a secondary aim was to test whether using an adaptive HTA (aHTA) approach that searched and transferred published evidence syntheses could increase the speed of HTA production.

The applied stepwise approach to the HTA is described in detail and can be summarized as defining a core team, topic selection, and prioritization; undertaking the HTA including adaptation using tools from the European Network for HTA (EUnetHTA) and stakeholder engagement; and concluding with dissemination.

The aHTA approach was faster but not as quick as anticipated, which is attributed to (i) the lack of availability of local evidence for contextualizing findings and (ii) the necessity to build trust between the team and stakeholders. Some delays followed from the COVID-19 pandemic, which showed the importance of good risk anticipation and mitigation. Lastly, other important lessons included the ability of virtual collaborations, the value of capacity strengthening initiatives within low- and middle-income countries (LMICs), and the need for early stakeholder engagement. Overall, the pilot was successfully completed.

This was the first HTA of its kind produced in Palestine, and despite the challenges, it shows that HTA analysis is feasible in this setting.

Evidence development for medical devices is often focused on satisfying regulatory requirements with the result that health professional and payer expectations may not be met, despite considerable investment in clinical trials. Early engagement with payers and health professionals could allow companies to understand these expectations and reflect them in clinical study design, increasing chances of positive coverage determination and adoption into clinical practice.

An example of early engagement through the EXCITE International model using an early technology review (ETR) is described which includes engagement with payers and health professionals to better inform companies to develop data that meet their expectations. ETR is based on an early evidence review, a framework of expectations that guides the process and identified gaps in evidence. The first fourteen ETRs were reviewed for examples of advice to companies that provided additional information from payers and health professionals that was thought likely to impact on downstream outcomes or strategic direction. Given that limitations were imposed by confidentiality, examples were genericized.

Advice through early engagement can inform evidence development that coincides with expectations of payers and health professionals through a structured, objective, evidence-based approach. This could reduce the risk of business-related adverse outcomes such as failure to secure a positive coverage determination and/or acceptance by expert health professionals.

Early engagement with key stakeholders exemplified by the ETR approach offers an alternative to the current approach of focusing on regulatory expectations. This could reduce the time to reimbursement and clinical adoption and benefit patient outcomes and/or health system efficiencies.

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.

Olaparib targets the DNA repair pathways and has revolutionized the management of metastatic castration resistant prostate cancer (mCRPC). Treatment with the drug should be guided by genetic testing; however, published economic evaluations did not consider olaparib and genetic testing as codependent technologies. This study aims to assess the cost-effectiveness of BRCA germline testing to inform olaparib treatment in mCRPC.

We conducted a cost-utility analysis of germline BRCA testing-guided olaparib treatment compared to standard care without testing from an Australian health payer perspective. The analysis applied a decision tree to indicate the germline testing or no testing strategy. A Markov multi-state transition approach was used for patients within each strategy. The model had a time horizon of 5 years. Costs and outcomes were discounted at an annual rate of 5 percent. Decision uncertainty was characterized using probabilistic and scenario analyses.

Compared to standard care, BRCA testing-guided olaparib treatment was associated with an incremental cost of AU$7,841 and a gain of 0.06 quality-adjusted life-years (QALYs). The incremental cost-effectiveness ratio (ICER) was AU$143,613 per QALY. The probability of BRCA testing-guided treatment being cost effective at a willingness-to-pay threshold of AU$100,000 per QALY was around 2 percent; however, the likelihood for cost-effectiveness increased to 66 percent if the price of olaparib was reduced by 30 percent.

This is the first study to evaluate germline genetic testing and olaparib treatment as codependent technologies in mCRPC. Genetic testing-guided olaparib treatment may be cost-effective with significant discounts on olaparib pricing.

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.

Poor nutrition links to chronic diseases, emphasizing the need for optimized diets. The EU-funded project PREVENTOMICS, introduced personalized nutrition to address this. This study aims to perform a health technology assessment (HTA) comparing personalized nutrition interventions developed through this project, with non-personalized nutrition interventions (control) for people with normal weight, overweight, or obesity. The goal is to support decisions about further development and implementation of personalized nutrition.

The PREVENTOMICS interventions were evaluated using the European Network for HTA Core Model, which includes a methodological framework that encompasses different domains for value assessment. Information was gathered via [1] different statistical analyses and modeling studies, [2] questions asked of project partners and, [3] other (un)published materials.

Clinical trials of PREVENTOMICS interventions demonstrated different body mass index changes compared to control; differences ranged from −0.80 to 0.20 kg/m2. Long-term outcome predictions showed generally improved health outcomes for the interventions; some appeared cost-effective (e.g., interventions in UK). Ethical concerns around health inequality and the lack of specific legal regulations for personalized nutrition interventions were identified. Choice modeling studies indicated openness to personalized nutrition interventions; decisions were primarily affected by intervention’s price.

PREVENTOMICS clinical trials have shown promising effectiveness with no major safety concerns, although uncertainties about effectiveness exist due to small samples (n=60–264) and short follow-ups (10–16 weeks). Larger, longer trials are needed for robust evidence before implementation could be considered. Among other considerations, developers should explore financing options and collaborate with policymakers to prevent exclusion of specific groups due to information shortages.

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.

Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia.

Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches.

Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety.

Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.

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.

To determine the level of awareness of health technology assessment (HTA) and its predictors among clinical year medical students in public universities in Klang Valley, Malaysia.

A cross-sectional study using the stratified random sampling method was conducted among clinical year medical students in four public universities in Klang Valley, Malaysia. Data on the level of awareness of HTA and its associated factors were collected using a self-administered online questionnaire. Descriptive, bivariate, and multivariate analyses were performed using IBM SPSS version 27 to determine the level of awareness of HTA and its predictors.

Majority (69 percent) of participants had a low level of awareness of HTA. The predictors of high-level awareness of HTA were attitude toward HTA (adjusted odds ratio (AOR) = 7.417, 95 percent confidence interval (CI): 3.491, 15.758), peer interaction on HTA (AOR = 0.320, 95 percent CI: 0.115, 0.888), and previous training on HTA (AOR = 4.849, 95 percent CI: 1.096, 21.444).

Most future doctors in public universities exhibit a low awareness of HTA. This study highlights the interplay between attitudes toward HTA, peer interaction, and previous training as influential predictors of HTA awareness. An integrated and comprehensive educational approach is recommended to cultivate a positive attitude and harness the positive aspects of peer interaction while mitigating the potential negative impact of misconceptions. Emphasizing early exposure to HTA concepts through structured programs is crucial for empowering the upcoming generation of healthcare professionals, enabling them to navigate HTA complexities and contribute to evidence-based healthcare practices in Malaysia and beyond.

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.

Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies.

The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP).

Data encompassing CONITEC’s official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected).

The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies’ experiences and government as demandant, potentially influencing CONITEC’s decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester.

NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.

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.

This study aimed to introduce a pilot program for hospital-based health technology assessment (HB-HTA) in China and present the participants’ experiences based on seven case studies from seven tertiary hospitals.

One-year pilot projects were initiated at the beginning of 2018. Seven pilot hospitals were closely followed from the beginning until the completion of their pilot HTA project. Regular interviews were conducted with the hospital managers leading the HB-HTA projects and key members of the special HTA teams. Observations were made based on field trips and written HTA reports.

Three pilot projects evaluated the use of medical consumables, three evaluated the use of surgical or medical interventions, and one evaluated an innovative management model for ventilators. Real-world data were collected from all the pilot projects to assist with the assessments. Most HB-HTA pilot projects achieved remarkable results such as improvements in economic efficiency; however, there were also obvious deficiencies such as the lack of a necessary cost-effectiveness analysis.

The results varied among the seven HB-HTA pilot projects. The HB-HTA pilot program was implemented to promote the use of HB-HTA in hospital decision making in China. At the same time, HB-HTA in China faces challenges. We have made some policy recommendations based on the findings of the pilot projects.