Research widely reports healthcare technologies contribute substantial environmental impacts. Timely health technology assessment (HTA) environmental sustainability (ES) framework development is critical. A lack of multisectoral expertise, resource constraints, and consensus to assess environmental impact is delaying methodological progression. The study objective was to identify and critically evaluate methods supporting ES in HTA and formulate recommendations to underpin the development of an ES framework in HTA.

This multisectoral systematic review followed PRISMA guidelines. Maximizing the opportunity to evaluate applied environmental assessment methods, the databases Web of Science, Embase, PubMed, IEEE Xplore, EBSCOhost GreenFILE, Cochrane Library, and International Network of Agencies for Health Technology Assessment (INAHTA) were searched between 2008 and 2023. Full-text published studies applying both qualitative and quantitative methods to evaluate technology ES were included. Frameworks were critiqued for their comprehensiveness based on sustainability scope and in challenging barriers to decision-making. Studies were ranked according to how transparent and feasible frameworks were in their ability to assess technology ES and alignment with HTA principles.

A total of 10 studies were identified and ranked in order of suitability in assessing technology ES in HTA. All studies applied a combination of methods to overcome issues such as data and resource constraints, expert knowledge, consensus in decision-making, and multiple criteria trade-off. Ranked highest, the One Health “extended” life cycle assessment (LCA) framework that utilized SimaPro LCA advanced software addressed the greatest methodological needs for HTA. Ranked second, the circular economy (CE) framework used in conjunction with an analytical hierarchy process (AHP) makes use of weighting and expert consultation to support multiple-criteria decision-making (MCDM).

This is the first systematic review to identify multidisciplinary frameworks, supporting evaluation of ES and decision-making in HTA. Providing valuable insight into potential methodological solutions where there is limited research, this study facilitates ES policy development in HTA. This study challenges limitations to methodological development highlighted by previous research. Further research should apply these recommendations in an HTA setting.

Disease-modifying therapies (DMTs) for Alzheimer’s disease (AD) are emerging treatment options. This study aimed to estimate the potential health system and associated environmental impacts of DMTs by modeling future bed-days and carbon dioxide equivalent (CO2e) emissions for the UK population under various scenarios for access to and efficacy of DMTs.

A cohort Markov model was developed to predict the UK population distribution from 2020 to 2040 across five health states—cognitively unimpaired and four stages of AD (mild cognitive impairment, and mild, moderate, severe dementia). These distributions were estimated using national population projections, AD prevalence data, and stage-specific transition rates. Annual bed-days per person for each state and associated CO2e emissions from published literature were applied to estimate total bed-days and emissions. Modeled scenarios combined ranges of DMT efficacy estimates (20 to 30%) and access levels (25 to 58% eligible patients receiving treatment) elicited from expert opinion to explore the extent of potential DMT impacts.

Without DMT access, annual bed-days across the four AD stages were projected to increase from 5.5 million to 8.6 million from 2020 to 2040, with cumulative bed-days totaling 140 million. Associated annual emissions increased from 0.7 Mt to 1.1 Mt CO2e, reaching 17 Mt CO2e cumulatively from 2020 to 2040. Under the various high-access (58% eligible patients treated) DMT efficacy scenarios, relative to no DMT access, annual reductions of 430 thousand to 650 thousand bed-days and 54 kt to 81 kt CO2e were estimated by 2040, and cumulative emissions decreased by 419 kt to 633 kt CO2e. Decreasing DMT access to 25 percent, assuming 25 percent DMT efficacy, reduced annual bed-days by 230 thousand by 2040, and annual emission savings decreased to 29 kt CO2e.

DMTs for AD may contribute to efforts by healthcare systems to reduce the carbon emissions from hospital inpatient care. Environmental sustainability should be considered as part of a holistic value proposition when assessing the benefits of new medicines.

Brazilian Unified Health System (SUS) Clinical Practice Guidelines (CPG) for prostate cancer recommend the use of androgen deprivation therapy (ADT) ± docetaxel for metastatic castration-sensitive patients (mCSPC). However, new medications have been developed since the last CPG update. We assessed the cost-effectiveness of abiraterone, apalutamide, darolutamide, and enzalutamide for mCSPC as part of the CPG update process.

A cost–utility analysis was built, with partitioned survival model composed of three states: progression-free, evidence of radiological progression, and death. The overall survival (OS) and progression-free survival (PFS) curves from docetaxel STAMPEDE trial were extrapolated using different distributions for a 20-year horizon. Visual inspection, clinical plausibility, and Akaike information criterion (AIC)/Bayesian information criterion (BIC) tests were considered to choose the best fit, which were adjusted by hazard ratios (HR) from indirect comparisons of interventions with docetaxel. Utility and disutility values were identified from the literature. Direct costs of medications and pre- and post-progression disease monitoring were considered. We considered a BRL120,000/quality-adjusted life year (QALY) (i.e., USD24,511) cost-effectiveness threshold.

When compared to docetaxel+ADT, interventions with higher effectiveness gains were abiraterone+docetaxel and darolutamide+docetaxel (0.94 and 0.99 QALY, respectively). Apalutamide and enzalutamide showed the highest incremental cost (BLR637,342 and BLR516,313, [i.e., USD130,187 and USD105,465], respectively). Abiraterone monotherapy or +docetaxel presented the lowest incremental cost–utility ratio (ICUR) (BLR84,960 and BLR79,428/QALY gained [i.e., USD17,354 and USD16,224], respectively) and may be cost effective for the SUS. Apalutamide, enzalutamide, and darolutamide may not be cost effective for the SUS as ICUR were higher than the cost-effectiveness threshold. Probabilistic sensitivity analyses or using different distributions for survival curves confirmed the direction of the findings.

Recent studies have demonstrated that adding docetaxel or antiandrogen drugs to ADT in men with mCSPC can improve OS and PFS compared with ADT alone. However, in this analysis, only abiraterone was cost effective, and the incorporation of apalutamide, darolutamide, and enzalutamide would require substantial price reductions to be cost effective for the SUS.

Sickle cell disease (SCD) is a rare disease, including renal complications. Erythropoiesis-stimulating agents (i.e., recombinant human erythropoietin, rHuEPO) are recommended for SCD and renal impairment. Evidence suggests its effectiveness in raising hemoglobin (Hb), which may reduce the need for regular blood transfusions. Cost-effectiveness analysis (CEA) and budget impact analysis (BIA) of rHuEPO were performed from the Brazilian Unified Health System (SUS) perspective.

A decision tree was created to assess rHuEPO’s impact on reducing blood transfusions. Quality-adjusted life year (QALY) for transfusion dependency and direct medical costs related to rHuEPO and transfusion were considered. SCD patients with worsening kidney function and Hb levels enter the model with an indication of receiving regular transfusions; those who proceed to the rHuEPO+standard care arm are likely to have clinically relevant elevation in Hb levels (i.e., <1.5 g/dL), suspending transfusions, and those who do not respond to treatment continue to receive regular transfusions. BIA population was estimated based on epidemiological data, considering direct costs over a five-year horizon.

rHuEPO+standard care compared to standard care generated 36.8 percent less need for transfusions, resulting in an increase of 0.033 QALY and a saving of BRL11,564 (USD2,362) per patient/year. rHuEPO was the dominant alternative; that is, there was greater clinical benefit and lower total cost. At BIA, the eligible population was 5,274 patients per year, on average. The direct cost of acquiring rHuEPO for the total eligible population summed BRL13,737,129 (USD2,806,016) in five years. However, considering the estimated effectiveness of CEA in reducing transfusions, BIA demonstrated savings of BRL96,545,791 (USD19,720,936) accumulated over five years.

Health technology assessments showing the new alternative as dominant are uncommon, especially in rare diseases, where patented orphan drugs usually have increased costs, which is not the case of rHuEPO. In this analysis, rHuEPO remained the dominant alternative and its incorporation would result in savings that may contribute to the sustainability of the Sistema Único de Saúde, Brazil’s national health system.

The inappropriate use of antimicrobial agents has been associated with increased healthcare costs and the spread of multidrug-resistant organisms. We aimed to estimate the economic impact of the developed antimicrobial stewardship program (ASP), after five years of implementation, versus the preliminary ASP, upon implementation, in the cancer setting at the National Center for Cancer Care and Research (NCCCR) in Qatar.

The research investigated the economic benefits of employing a preliminary ASP versus a developed ASP from the perspective of a public healthcare hospital. Preliminary ASP was defined as the 12 months following the establishment of the ASP (i.e., 1 May 2015 to 30 April 2016), while developed ASP was defined as the most recent 12 months of ASP implementation (i.e., 1 February 2019 to 31 January 2020). Patient records were retrospectively reviewed. The total economic benefit of ASP maturity was calculated as the sum of the cost savings and the cost avoidance associated with the service, minus the operational cost.

A total of 1,000 patients were included in the study. The developed ASP was associated with substantial reduction in antimicrobial consumption and resource utilization. Total cost of resources to avoid during the developed ASP period was USD1,634,658, in contrast to USD4,923,024 during the preliminary ASP period, yielding a positive cost avoidance of USD3,288,366. Developed ASP incurred lower operating costs than preliminary ASP, resulting in positive change in operational cost of USD3,428. The benefit-to-cost ratio was 640. The net benefit due to ASP maturity was USD3,624,875. The robustness of the results is demonstrated by the sensitivity analysis.

This study underscores the benefits of ASP development in a cancer setting. The observed reductions in antimicrobial costs from reduced antimicrobial use and the added value of cost avoidance signify the positive impact of a well-developed ASP. These findings lend support to the broader implementation of comprehensive ASPs, which contribute not only to patient-care optimization but also to cost containment.

The National Institute for Health and Care Excellence (NICE) conducts health technology assessment to assess cost-effectiveness and budget impact. For treatments provided in vials, NICE often considers how treatments are dispensed and adjusts the economic modeling costs accordingly. Vial sharing and wastage are likely familiar concepts to stakeholders, but the same consideration is not consistently given to tablet packs.

Using anonymized examples, NICE assessed potential implications for cost-effectiveness and budget impact of different methods for modeling oral treatments. Firstly, the cost-effectiveness and budget impact were calculated based on a cost per milligram (mg) of treatment. The per mg cost was multiplied by the number of mg for each dose and did not account for the number of tablets or packs required. Using the same example, the cost per tablet was calculated by rounding each dose to the nearest whole tablet mg dose. Finally, the example was costed based on whole packs, which included the cost for any wasted tablets.

The anonymized examples showed that costing per mg versus per tablet versus per pack can have a significant impact on cost-effectiveness and budget impact. One example showed that treatment costs per 28 days could increase by over GBP1,000 (USD1,271) when costing per whole pack compared to per mg. This led to a difference in the incremental cost-effectiveness ratio (ICER) of nearly GBP10,000 (USD12,716). Another example demonstrated a potential increase in budget impact of nearly GBP1 million (USD1.27 million) per year. This magnitude of impact on cost-effectiveness and budget has the potential to change health technology assessment decisions and affordability in the United Kingdom.

NICE is assessing an increasing number of oral treatments provided as tablet packs, not vials. This highlights the need to consider how pack sharing and wastage should be consistently considered in economic modeling. Developing standardized methods for modeling oral treatments would help ensure consistency of cost calculations and better reflect how treatments are dispensed in clinical practice.

Pharmaceutical companies have been actively taking early scientific advice from health technology assessment (HTA) agencies during development, focusing on study design to understand the HTA evidentiary requirements. The evolving advice landscape, including multistakeholder and international collaborations, highlights proactive engagement’s importance. This opinion survey assessed international pharmaceutical companies’ current experiences in seeking early HTA and explored strategies for forward-looking actions and considerations.

An opinion survey was designed and conducted in 2023 as a cross-sectional questionnaire consisting of multiple-choice questions. The questionnaire provided a qualitative assessment of companies’ current strategies and experiences in taking early HTA advice as well as future considerations. Eligible survey participants were the senior management of Global HTA/market access departments at 22 top international pharmaceutical companies.

Responses were received from 13 companies. The National Institute for Health and Care Excellence (NICE) or the Federal Joint Committee (Gemeinsamer Bundesausschuss, G-BA) were utilized mostly for early HTA advice (92% respondents). Past European Medicines Agency HTA experience exists (50%), but there is no current engagement in Joint Scientific Consultation (JSC). However, JSC is deemed a top priority (83%) given the Regulation (EU) 2021/2282 on health technology assessment. Challenges in seeking advice include agency availability and internal resource and timing constraint. Advice-seeking actions mostly occurred during phase II trials but were frequently limited by agencies’ availability (84% respondents). Divergences were identified regarding eligibility criteria to seek advice and internal practices. Five success indicators were identified with the top-rated being the impact on development plan.

This survey assessed companies’ practices in seeking early HTA advice, with most engaging national agencies like NICE and G-BA. The lack of current JSC participation, despite companies’ prioritization, highlights the agencies’ need to enhance capacity and resources. Survey results underscored the importance of companies’ adaptive strategies in the evolving environment, which can be supported by active measures of advice success.

In collaboration with a European Reference Network for rare diseases, we aimed to identify red flags for the diagnosis of rare and complex connective tissue and musculoskeletal diseases (rCTDs). Some indicators, presented as red flags, might raise clinicians’ awareness about the presence of rCTDs. Their identification is critical in primary care, where they are most likely to be first observed.

Firstly, we conducted a scoping review to identify red flags already published in the scientific literature. We included studies about people with rCTDs that described red flags, warning signs, alarm symptoms, and pathognomonic signs identifiable in a primary care setting. Then, we conducted a systematic review of evidence pointing out which signs and symptoms should arouse suspicion specifically for IgG4-related disease. We included studies providing estimates of diagnostic precision or prevalence of signs and symptoms, and we assessed their quality and applicability to the review question. We conducted systematic searches in major medical databases and manual searches in rare disease resources.

For the scoping review, 49 studies out of 1,656 records met the inclusion criteria. Two reported red flags for autoimmune diseases altogether, and 14 described red flags for systemic sclerosis. For the systematic review, seven studies out of 4,477 records met the criteria, comprising five diagnostic precision studies and two large case series. These were generally rated as having a high risk of bias and were included as indirect evidence. We identified 32 potential IgG4-related disease red flags, 10 related to clinical history findings and basic signs or symptoms, and eight belonging to common laboratory findings and basic imaging techniques.

Red flags for rCTDs have generally been established through expert consensus and lack valid indicators for diagnosis, such as sensitivity, specificity, or predictive values. They frequently overlap among different rCTDs. Potential red flags are prone to change as further evidence emerges. This shows the need to collaborate with reference networks to address rare diseases where the evidence is still scarce.

Conducting a systematic review (SR) of clinical trials is labor-intensive and expensive. However, existing open-source content can be used to develop custom machine learning tools suited to the workflow of individual organizations. This case study details the potential of a bespoke tool developed by York Health Economics Consortium (YHEC) for reducing the time and cost involved in producing an SR.

RESbot is a flexible, stand-alone machine learning tool created using an extensively tested open-source dataset developed by Cochrane. The tool identifies randomized controlled trials (RCTs) from a large corpus of records. It has a user interface and inputs/outputs to fit into the company’s existing workflow at any stage. RESbot has two settings. The “sensitive” setting identifies a higher number of possible RCTs with a lower risk of missing eligible studies, while the “precise” setting is more focused. For both settings, we estimated the reduction in resources required for record screening in two examples of RCT-only reviews.

Scoping searches in MEDLINE were conducted for SRs of RCTs in femoropopliteal artery diseases (FAD) and postpartum depression (PD). The results were run through RESbot. For the FAD SR, 1,444 references were retrieved, with the sensitive and precise RESbot settings reducing the record set by 38 percent and 64 percent, respectively. For the PD SR, a record set of 2,153 records was reduced by 25 percent and 41 percent, respectively. Resource savings offered by RESbot vary depending on subject but may reduce the time taken to screen records by up to 64 percent, with a subsequent reduction in cost to the organization commissioning the SR.

The use of bespoke machine learning tools in SR production has the potential to reduce the time and staff costs involved in producing a review. This case study tested the effect on a small number of records, but for larger reviews retrieving tens of thousands of records, reductions in time and costs can be very significant.

In Ghana, the reimbursement of iron polymaltose complex (IPC) for iron-deficiency anemia treatment raises concerns as a potential cost driver for the National Health Insurance Scheme (NHIS). Prioritizing value for money is crucial for NHIS sustainability. With Ghana’s established health technology assessment (HTA) framework, we provide insights and preliminary findings in this case study on anemia treatment.

To further support institutionalization, we follow the Ghana HTA process guidelines and Ghana HTA reference case. Simultaneously, to build wider capacity for HTA in Ghana, relatively new trained staff was engaged while anchoring the team at the HTA Secretariat from the Ministry of Health. We started a situational analysis, which includes desktop review and key-stakeholder interviews. An umbrella review was commenced simultaneously to identify systematic reviews assessing clinical effectiveness of IPC compared to other medicines on the essential medicine lists for all population groups. We will assess cost-effectiveness to ultimately inform coverage decisions and possible implications for organizational structures.

Anemia is a persistent public health issue in Ghana, impacting children under five, young women, and expectant mothers. The essential medicine list includes various treatments, but ambiguity exists in the standard treatment guidelines regarding when to administer IPC versus alternative medicines. Under the NHIS, the intravenous formulation of IPC stands out as the most expensive treatment. A systematic review identified three papers, focusing on infants, children, adults, and pregnant women. Preliminary findings suggest weak overall evidence supporting IPC’s superiority, although adults may exhibit higher tolerance for IPC compared to alternative treatments.

Conducting an HTA on anemia treatments, despite the low treatment costs, is still significant as technical efficiencies can be achieved with high-volume drugs by analyzing prescription patterns and generating evidence to support potential changes. Findings from this HTA can be used elsewhere, especially in resource-constrained settings. Next steps involve finalizing the HTA and disseminating results to stakeholders.

The decision-making process for health technology assessment (HTA) in ultra-rare diseases faces a significant challenge for agencies worldwide. This study sought to offer an analytical overview of the clinical evidence outlined in the recommendations of the Brazilian National Committee for Health Technology Incorporation (Conitec) in ultra-rare diseases.

Data were extracted from recommendation reports for the ultra-rare diseases evaluated between 2012 and 2022. To classify a disease as ultra-rare, the epidemiological criterion or a consultation with the Orphanet platform was used (prevalence of ≤1/50,000 inhabitants). The extracted variables included the type of evidence synthesis, type of studies, instrument, the result of the assessment of the methodological quality of the studies, the format of evidence synthesis presentation, whether the evidence was graded, and the result.

Among 53 analyzed reports, 70 percent relied on randomized controlled trials, followed by systematic reviews (SR), and observational studies. Reports with positive recommendations based on SR comprised 63 percent. GRADE applied to 27 reports and indicated low or very low results for the first two outcomes (62% and 65%). No clear link between evidence quality and final recommendations was observed. Meta-analysis-based reports had 83 percent positive recommendation rate, compared to 55 percent without meta-analysis. Surrogate outcomes were predominant. Clinical characteristics significantly influenced final decisions, especially when new data emerged in public consultation or had the potential to alter disease progression, reduce severe events, or enhance survival.

Ultra-rare diseases pose challenges in evidence quantity and quality. Traditional HTA frameworks seem inadequate, lacking robust evidence for these conditions. The difficulties in ultra-rare disease HTA underscore the need for specialized frameworks. This analysis acknowledges limitations, notably the heterogeneity in older report structures compared to recent ones, reflecting evolving HTA methodologies in Brazil.

There needs to be more awareness and agreement on value criteria important to digital health stakeholders (DHSs), including health technology assessment agencies, especially in low- and middle-income countries (LMICs). To overcome these challenges, we focused on the feasibility of a dashboard to improve low confidence and assessment readiness of digital health technologies (DHTs) among DHSs in Malaysia.

“Artificial Intelligence Embedded X-ray for Lung Cancer Screening” from the Malaysian Health Technology Assessment Section (MaHTAS) was selected as a DHT use case. Representatives from MaHTAS, health informatics, research and innovation agencies, the digital health industry, patient advocates, and bioethicists attended an information briefing session and workshops. Participants completed a value assessment checklist questionnaire on the completeness of the information for the use case. Data and insights from these online workshops were incorporated into the development of Figma dashboard mock-up. Feedback from participants, other Malaysian DHSs, additional LMICs, and high-income countries was incorporated into the mock-up.

For the use case, average ratings from participants on the completeness of information on the seven value domains (VDs) were 3.4 and 3.1 for “healthcare system challenges and current use of technology” and “effectiveness” out of a maximum rating of 4 for “sufficient.” Both VDs were classified as “partially sufficient.” Ratings for the remaining VDs were 2.9 on “technical product information and use,” 2.8 on “safety,” 2.5 on “cost and economic impact,” 2.2 for “usability and accessibility,” and “ethical aspects.” These VDs were classified as “not sufficient.” The dashboard and its use were evaluated positively by 17 of 22 DHSs, including participants.

It was feasible to develop a useful dashboard applying a value assessment framework for DHTs on a use case with clear visualizations and a systematic approach. Our results have potential for applications in early scientific advice. It is to be considered that workshop participants’ profiles may create a bias regarding their subjectivity in rating completeness of information on a DHT.

The Chinese government lifted most COVID-19 pandemic restrictions in December 2022, triggering a spike in confirmed cases and higher demand for medications. Consequently, a significant number of residents resorted to social media to seek assistance. This study aimed to evaluate community resilience by leveraging Weibo user datasets, coupled with interpretable machine learning (ML)-based techniques, to identify important resilience characteristics.

Datasets geotagged from the Sina Weibo social media platform between 8 December 2022 and 7 January 2023 were crawled using search terms of “help-seeking” and the keywords of conventional drugs. This study utilized natural language processing (NLP) to label COVID-19-related posts to identify the type of posts, stakeholders’ behaviors, and other information. We built a comprehensive evaluation model, and five ML-based algorithms were compared for analyzing community resilience. Local interpretable model-agnostic explanations (LIME) was employed to verify five models and the XGBoost algorithm showed optimal effects. Shapley Additive Explanations (SHAP) elucidated the best model’s outputs and estimated contributions for key resilience characteristics.

For this study, 199,709 posts were collected. Out of these, 48,425 posts were identified as help-seeking posts, with more than two-thirds receiving responses from community level. The area under curve (AUC) of the XGBoost model was 0.82 (95% confidence interval [CI]: 0.82, 0.83), and the values of accuracy and F1 score were 0.72 and 0.80, respectively. This result demonstrated that the model can successfully evaluate community resilience and subsequently identify the features driving this outcome. Collective efficacy in providing aid, support from official rescue guidelines, and residents’ rapid response to rescue information were identified as the most important characteristics for evaluating community resilience.

This study is the first to harness social media data to quantify community resilience in China based on a framework we developed. Five updated ML-based algorithms were developed to evaluate community resilience, and XGBoost showed optimal effects. Three characteristics of community resilience were found as potential predictors that can enhance decision-making support to reshape health emergency rescue activities.

Findable, structured, and understandable data from health technology assessment (HTA) reports is the core of HTA policy research. Available databases with this information, such as the International Network of Agencies for Health Technology Assessment (INAHTA) database, may be incomplete and their common manual data collection is time-consuming. Automated data extraction may offer a solution by creating a standardized, real-time-updating, comprehensive, open-access HTA database.

In this research, we explore the possibilities of automated data extraction in the context of creating a standardized and comprehensive HTA policy research database. Data points were extracted from publicly available guidance reports of the National Institute for Health and Care Excellence (NICE) using different text extraction techniques such as natural language processing (NLP) and generative pre-trained transformers (GPTs). Future efforts are aiming to expand the database to other HTA bodies and link it to the European Medicines Regulatory Database (EMRD) that is also being developed.

Preliminary results of our research show that it is possible to use existing text extraction techniques to extract relevant information from publicly available HTA recommendations. Scaling the system to include more HTA bodies and data points is challenging as extraction based on document structure is complicated by heterogeneity in document structure within HTA bodies and between HTA bodies. Future results will focus on finding the best data extraction approach for each data point and on validating the system.

Using automated data extraction to extract data from HTA reports can be a viable option for creating a comprehensive database that can be used to enhance comparative HTA policy research. Challenges remain in scaling the system to include more HTA bodies and data points. Results regarding best-performing extraction techniques and data validation of the system are expected soon.

Artificial intelligence (AI) is transforming healthcare decision-making, particularly in evidence evaluation and health technology assessment (HTA). This research explores challenges and ethical considerations associated with AI implementation, and biases. It highlights the need for diverse stakeholder perspectives and collaboration to ensure responsible AI use. Through transparency, accountability, and bias mitigation, AI has the potential to revolutionize decision-making and improve patient care while promoting equitable outcomes.

Literature research was conducted, including peer-reviewed studies and grey literature, using the PEARL search strategy. Relevant articles from various databases and sources were screened and selected based on their alignment with the research objectives. The selected articles were then analyzed to identify key findings and insights related to the integration of AI in healthcare decision-making, ethical considerations, bias mitigation, and stakeholder perspectives.

The literature research revealed that AI in healthcare decision-making holds great promise. AI algorithms can efficiently analyze diverse healthcare data sources, improve evidence evaluation, and streamline decision-making processes. Ethical considerations, patient privacy and transparency are crucial. Bias in AI algorithms emerged as a significant challenge, requiring diverse and representative data, bias testing, and explainable AI. Stakeholder engagement plays a vital role in responsible AI implementation. Strategies for ongoing monitoring, collaboration, and training were identified to ensure fair and ethical decision-making in healthcare. The results emphasize the need for a balanced approach to harness the potential of AI while addressing its challenges.

Integration of AI in healthcare decision-making offers promising opportunities but also presents challenges that need to be carefully navigated. By addressing ethical considerations and mitigating bias, AI can revolutionize decision-making, improve patient outcomes, and ensure the responsible and ethical use of AI in healthcare. The results provide valuable insights and recommendations for researchers working in the field of HTA.

There is no comprehensive framework that considers the various features of current funding models on drug accessibility for rare diseases; such a framework would assist policymakers to more effectively meet the challenges of these patients. This article reviews the funding models implemented worldwide to facilitate this access.

The PRISMA guidelines were used to conduct a systematic literature review. The following databases were searched: Ovid (Embase/MEDLINE), Cochrane database, Web of Science, EconLit, the National Institute for Health and Care Research (NIHR), Centre for Review and Dissemination (CRD), and International Network of Agencies for Health Technology Assessment (INAHTA). Two independent reviewers screened all titles and abstracts, and one reviewer did the full-text review and data extraction. Data were collected on general study characteristics, general aspects of rare diseases, source of funding, allocation of resources, and pricing strategies.

A total of 3,815 unique citations were screened, and 148 were included for data extraction. Each funding model was characterized based on its unique features specific to rare diseases, focusing on process, methods applied, and consideration of attributes. Sixty funding models were identified in 41 countries, categorized as separate processes (42%), exceptions to standard processes (32%), standard processes with no changes (23%), and alternative pathways (3%). More than one funding model was available for 29 percent of countries. Funding models varied in their approach to HTA, source of funding, consideration of uncertainty, and pricing strategies.

The diversity of funding models highlights the complexity of addressing access to treatments for rare diseases. Special considerations towards rare diseases generally targeted the greater uncertainty in the clinical evidence. Despite the existing platform that enables access for drugs for rare diseases, only 10 percent of rare diseases have an available treatment and fewer patients can access these technologies.

Evidence on re-appraisals of health technologies in Germany is limited, and for rare disease treatments (RDTs) the Federal Joint Committee (GBA) uses different processes depending on whether the annual revenue threshold has been exceeded. We analyze RDTs for which an initial appraisal and a reappraisal were conducted to better understand processes used to determine the clinical benefit rating and their outcomes.

We review appraisal documents for 55 RDT indications published between January 2011 and September 2023. We extract information for the change in the maturity of survival data, the type of evidence, the risk of bias, and the availability of additional evidence as proxies for evidence quality. Specifically, we review the reasons for conducting reappraisals; examine how evidence quality and the clinical benefit rating differed between initial appraisals and re-appraisals; and explore the association between evidence quality and the clinical benefit rating following reappraisal.

Most reappraisals were conducted because of exceeding the revenue threshold of EUR50 million (USD54 million) per year or reaching the review date when an initial decision was time limited. Almost all initial appraisals used the limited process, while the majority of reappraisals used the regular process. While nine out of 55 reappraisals achieved a higher benefit rating in reappraisals compared to initial appraisals, in 21 out of 55 reappraisals the benefit rating decreased. There was some evidence that reappraisals with accepted randomized controlled trial evidence were significantly more likely to achieve a higher clinical benefit rating.

Our findings confirm that reasons and processes for completing reappraisals of RDTs in Germany differ. Moreover, the quality of the evidence submitted for both initial appraisals and reappraisals of RDTs was limited and achieving a high clinical benefit rating in reappraisals was rare.

The decision-making process for health technology assessment (HTA) in ultra-rare diseases is a global challenge. Establishing a comprehensive analytical framework for these unique diseases poses difficulties. This study aims to descriptively analyze arguments reported by the Brazilian National Committee for Health Technology Incorporation (Conitec) in deciding whether to include technology for ultra-rare diseases.

Data from recommendation reports (2012 to 2022) were analyzed. Diseases with a prevalence of fewer than one per 50,000 inhabitants were classified as ultra-rare. Extracted variables included preliminary and final recommendation results and justifications by Conitec. Six argument categories were created (method-related issues; evidence; cost; technology effectiveness or safety; context; innovation). Word clouds were generated based on word frequency in each category to present the data.

In the analysis of 45 reports, the word clouds highlight frequent terms in favorable arguments, emphasizing evidence quality, cost reduction, and applicability in the healthcare system. Conversely, unfavorable arguments also revolve around evidence quality and cost impact. The analysis of the arguments according to categories, 16 arguments were identified: seven concern evidence issues, five cite methodological problems in presented studies, four relate to costs, and three pertain to technology effectiveness or safety. Unfavorable arguments primarily stem from evidence-related concerns. In favorable arguments, cost (seven) and safety (six) are prominent, with innovation (one) and context (three) being additional categories not found in the unfavorable group.

While technology assessment processes for ultra-rare diseases have evolved, the justifications for recommending or not incorporating new technologies remain unchanged. Over time, reports have become more detailed, focusing on evidence and methodological specifics. This highlights the importance of scrutinizing evidence characteristics and determining relevant criteria and data types for this unique context.

Rare diseases (RD) can be severe and can dramatically reduce life expectancy and quality of life. RD therapies, mostly orphan drugs (OD), fail to meet the standard criteria for public reimbursement due to uncertainty in cost-effectiveness estimates limiting healthcare access. To address this issue, experts have suggested the integration of social preferences into health technology assessments (HTAs) by implementing different methods.

This systematic literature review, performed in 2021, aimed to explore worldwide experiences of social preferences integration into HTAs for RD and OD through the implementation of multiple-criteria decision analysis (MCDA), discrete choice experiments (DCE), and person trade-off (PTO), among other methods. A systematic search of the literature was conducted using PubMed, Cochrane, Embase, and Scopus databases. The PRISMA approach was used for the review phases. Finally, the Promoting Action on Research Implementation in Health Services (PARIHS) framework was used to discuss the implementation of these instruments in the RD context.

Thirty-three articles met the inclusion criteria. The studies measured social preferences for RD and OD as part of HTA using MCDA (n=17), DCE (n=8), and PTO (n=4), among other methods (n=4). These found that patients and clinicians do not prioritize funding based on rarity. The public is willing to allocate funds only if OD demonstrates effectiveness and improves the quality of life (QoL), considering as relevant factors disease severity, unmet needs, and QoL. Conversely, HTA agency experts preferred their current approach and placed more weight on cost-effectiveness and evidence quality even though they expressed concern about the fairness of the drug review process.

MCDA, PTO, and DCE, among others, are helpful and transparent methods for assessing social preferences in HTAs for RD and OD. However, their methodological limitations, such as arbitrary criteria selection, subjective scoring methods, framing effects, weighting adaptation, and value measurement models, could be hurdles to implementation. Further research is needed to tailor these methods’ applicability and impact in different social contexts.

The American College of Medical Genetics and Genomics (ACMG) recommends that pathogenic variants linked to 37 genetic diseases be disclosed as secondary findings (SFs) to patients undergoing genome-wide sequencing (GWS), including rare diseases (RDs) treated with enzyme replacement or gene silencing/replacement therapies. We estimated the potential budget impact of treating these RD patients in the US and Canada.

A population-based model was used to estimate the number of patients that would be identified in a given jurisdiction for the 11 diseases on the ACMG’s SFv3.2 list classified as “inborn errors of metabolism” or “miscellaneous phenotypes.” Genetic and clinically diagnosed prevalence was estimated for each gene-phenotype pair based on the scientific literature. Demographic and GWS utilization data were obtained from government websites and payer reports. Drug costs were obtained from the literature and payer websites and reported in 2023 US dollars. A range of one-way sensitivity and scenario analyses were conducted, and province- and state-level estimates were also generated.

Based on an estimated current annual GWS utilization rate of 77 per million in the US, 237 RD SFs would be identified annually (0.9% of 25,663 patients tested), out of which 107 (45%) would be variants linked to hereditary transthyretin amyloidosis (hATTR). In Canada (GWS utilization rate of 169 per million), 45 patients would be identified per year, of which 11 (24%) would be hATTR. Treating 50 percent of hATTR SF patients with patisiran would cost approximately USD26.0 million in the USA and USD2.8 million in Canada annually. In contrast, no cases of RPE65-related retinopathy would be detected at current utilization rates.

The addition of hATTR (with an estimated genetic prevalence of one in 240 in the US) to the ACMG SF list may result in a significant number of additional cases diagnosed per year. While there is an ongoing debate about initiating presymptomatic treatment, reimbursement requests for high-cost drugs like patisiran are likely to increase if GWS utilization continues to grow.