In recent years, there has been a growing recognition that health equity and health inequalities should be a consideration in all aspects of research. Since the Commission on Social Determinants of Health by the World Health Organization was established in 2005, there has been a growing interest in tackling systemic differences in health outcomes, including expanding the scope to health research including evidence synthesis and health technology assessments (HTA). This analysis aims to identify health inequality and health inequity frameworks that exist to help structure and plan research methods in evidence synthesis.

A critical analysis of the existing frameworks used in evidence synthesis to address health inequality and/or inequity was undertaken. Comprehensive, systematic searching of seven social science electronic databases and grey literature was undertaken based on the Behavior/phenomenon of interest, Health context and Model/Theory (BeHEMoTh) model, from 1990 to May 2022 to identify all relevant studies. A narrative synthesis approach was used to critically appraise the existing frameworks.

Sixty-two reviews published between 2008 and 2022 reporting on using a framework to stratify health opportunities and outcomes met the inclusion criteria. Frameworks identified included the PROGRESS (place of residence, race or ethnicity, occupation, gender, religion, educational level, socioeconomic status, and social capital), PROGRESS-Plus (plus age, disability and sexual orientation) and Preferred Reporting Items for Systematic Reviews and Mata Analysis (PRISMA) – Equity checklist.

Currently, there does not seem to be consensus in how evidence of inequality or inequity in evidence synthesis or HTA are reported. As research interests in health inequality and inequity continue to grow, there is a need to develop a framework that provides an in-depth understanding of how inequalities in health and inequities in health should be considered within evidence synthesis and HTA. This will allow researchers to analyze not just the effects of interventions, but also how healthcare outcomes are impacted by inequalities or inequities.

Threshold analysis is a novel statistical approach which can be used to investigate which direct comparisons in a network meta-analysis (NMA) have estimated relative effects that may not be robust to changes in the evidence, either due to possible bias, sampling variation, or relevance.

In a health technology assessment of the clinical effectiveness of ablative and non-invasive therapies for patients with early hepatocellular carcinoma (HCC), we conducted a threshold analysis to identify treatment comparisons that would be sensitive to changes in the randomized controlled trial (RCT) evidence used in the NMAs, potentially leading to a change in the recommended treatment. The results of the threshold analysis were used to guide a targeted systematic review of high-quality, non-randomized, prospective comparative studies that could strengthen the evidence for those comparisons identified as sensitive to change.

We conducted NMAs of RCT evidence for four outcomes: overall survival (16 RCTs), progression-free survival (6 RCTs), overall recurrence (7 RCTs), and local recurrence (10 RCTs). The results of the NMAs displayed a high level of uncertainty, attributable to the sparse nature of the network, characterised by interventions being mainly compared in small trials. A targeted systematic review was conducted on relevant interventions that were identified as being sensitive to changes in evidence by the threshold analysis. The studies identified in this review were incorporated into a second NMA to support the RCT evidence.

Threshold analysis has been typically used as a tool to assess how robust comparisons in an NMA are to additional sources of evidence, but it can also be used to guide the search for additional non-randomized evidence when the available RCT evidence is sparse.

Patient involvement in health technology assessment (HTA) has documented advantages, such as improved understanding of disease context, and increased legitimacy and transparency of the HTA process. In the absence of clear metrics, thresholds, or criteria, it is not clear how input regarding patient preferences influences HTA based recommendations of the pan Canadian Oncology Drug Review (pCODR).

This is a concurrent complementary mixed methods study. A quantitative model (logit) is used to estimate the impact of patient input and other HTA criteria on pCODR recommendations. A qualitative analysis of semi-structured interviews with Canadian HTA committee members is used to describe the mechanisms of action through which patient input influences recommendations.

Patient input was considered important in providing context to the HTA discussion, but committee members were not able to explicate how any specific elements of patient submissions weighted into the committee’s recommendation. There was an element of mistrust in the patient input data. The estimated impact of patient input on funding recommendations is not statistically significant, recommendations remain driven by evidence of clinical benefit.

The commitment to inclusion of patient perspectives in HTA in Canada is strong, and procedurally Canada is among the leaders in this regard. The tangible impact of patient input could be increased with an improved system for collection of most relevant data, and clear guidelines about how patient input should weigh into HTA recommendations.

Health technology assessment (HTA) is defined as a multidisciplinary field of policy research that provides evidence on the consequences of adopting and using health technologies. A ministry of health with jurisdiction over HTA should determine the influence of public law on all HTA-related activities and the rules that apply. Therefore, health decision-makers interested in HTA must learn to navigate the legal system, starting by situating it in the legal apparatus of the country. As a result, establishing a national HTA system requires designing a legal pathway towards HTA. However, a historic overview of HTA, in the context of policy documents of Ethiopia is not clearly reported. Therefore, this review is warranted to understand the historic overview of HTA in the context of health policy documents of Ethiopia.

A review of online policy documents was conducted in advanced Google Scholars and websites of the Ministry of Health (MoH) of Ethiopia. Some of the policy documents were also obtained through contacting experts at MoH. The review findings were organized into six categories.

Regulatory documents have emphasized the approval of new health technologies before selecting health technologies. Health Policy of Ethiopia and the Directive on Medical Equipment also clearly stated the importance of institutionalizing HTA and establishing HTA organizations. Additionally, the National Medical Device Policy clearly indicated the importance of establishing an HTA advisory team at MoH. Similarly, the Health Sector Transformation Plan II (HSTP II) stressed the need to build a national capacity to conduct HTA. Even though policy statements on HTA appear scattered across different policy documents, they were not put into a national HTA system.

It is important to refer to policy statements outlined in different policy documents when establishing a national HTA system in Ethiopia. Also, attention should be given to the development of policy documents related to HTA guidelines, strategic documents in HTA, and policy documents that can link HTA results to policy-making.

The healthcare sector in the Asia Pacific (APAC) region is in a period of rapid growth and exciting innovation. This has led to an increase in the number of APAC countries adopting and implementing health technology assessment (HTA) to assess the clinical, ethical, economic, and societal aspect of healthcare technology. The aim of this study is to provide an updated snapshot of the status of HTA and key differences in selected countries of APAC region.

HTA robustness and gap requirements, including its structure, process, use in decision-making and resource allocation processes were assessed through a review of published and gray literature for each of the selected country. A qualitative analysis was carried out by using a set of 15 principles of an International Working Group for HTA Advancement to identify the robustness and key differences in HTA based decision-making process in scoping countries.

The finding of this study reveals that maturity of HTA determined by country-specific factors, such as presence of independent HTA agency, healthcare funding and expenditure, etcetera, and varies across the high-, middle-, and low-income countries of APAC region. Based on the study’s results, HTA ecosystem of selected countries categorized into rising HTA followed by advancing and mature HTA categories. In addition to the differences in HTA structure, the influence of stakeholder engagement differs among HTA bodies. The variation in the time frame of HTA decisions was significant among countries, with a general lack of awareness and transparency among health policy decision-makers and resulted in longer time for assessment for rising HTA category compared to the advance and mature HTA categories.

The vision of a comprehensive and robust HTA system can be achieved by implementing a transparent, independent, decision-making, and strongly integrated HTA process in the region. We recommend that efforts should be directed to promote a transparent and sustainable HTA, throughout the low- and middle-income countries of APAC region which eventually, lead to more effective HTA ecosystem.

With ever increasing burden of disease and limited resources, health technology assessment (HTA) is required for efficient resources allocation and priority setting in healthcare. The objective of this study was to establish the baseline HTA evidence generation and use in Zimbabwe.

In 2019, we convened a stakeholder workshop on HTA at the University of Zimbabwe. Presentations on HTA processes, current healthcare reimbursement model, priority setting in the Ministry of Health and selection of medicines into the treatment guidelines, were done by the experts. We adapted the Health Intervention and Technology Assessment Program questionnaire for situational analysis of HTA introduction at national level and administered it among the workshop participants. We report the baseline information on HTA situation, the need, demand and supply of HTA in Zimbabwe obtained from the presentations and responses from workshop participants.

A total of 33 participants attended the workshop. Participants indicated that there is no formal HTA agency or process in Zimbabwe. The selection of medicines into treatment guideline is determined by disease burden, safety, efficacy and cost data, and it is done by a group of experts. The Association of Healthcare Funders of Zimbabwe (AFHOZ) reported that private healthcare funders use resource-based relative value scale system to determine tariffs and reimbursement levels. The regulator requires safety, efficacy and product quality data for the registration of medicines. Transparency in decision-making, registration of heath technology and formulation of essential medicines and treatment guidelines were reported as the major needs of HTA. The major users of HTA outputs were reported as medicines regulator, AFHOZ and Ministry of Health. Key suppliers of HTA evidence are academic and clinical research institutions and healthcare workers. Lack of training in health economics was cited as the major challenge to supply of HTA evidence.

There is a need to institute a formal, systematic and transparent processes of determining value of health technologies.

An iterative, life-cycle approach to the evaluation of healthcare technologies requires that clinical and economic evidence is collected since the initial stages of diffusion. Nevertheless, early cost-effectiveness models are challenging mainly due to the difficulties in estimating model parameters and faithfully characterizing parameter uncertainty. This is especially true with AI-based diagnostics, where attribution of effects on costs and patient-relevant outcomes is more challenging. Empirical applications of early-models are useful to identify the main challenges of iterative modelling and provide recommendations on best-practices. Here, we reported on a case study on a machine learning-derived hypotension predictive index (HPI), that predicts the onset of intraoperative hypotension and trigger corrective measures.

A hybrid decision-tree/Markov model was developed comparing an HPI-based intervention protocol to standard-of-care intervention protocol during gynecological procedures. A short-term component of the model was populated using data from individual patients collected at one hospital in Italy. An historical control group was also defined using propensity score matching. Long-term costs and consequences of HPI were modelled using secondary data. A probabilistic version of the headroom approach was used to determine the maximum achievable price of HPI based on available evidence. Value of Information analysis was also conducted to identify the parameters that contribute the most to the overall uncertainty, and to identify optimal future study designs. Extensive deterministic and probabilistic sensitivity analyses were conducted to characterize the uncertainty over the cost-effectiveness of HPI.

The preliminary results of the analysis suggest that HPI has potential to improve patients’ outcomes and generate efficiency gains by reducing hypotension events and permanent complications, such as acute kidney injury. The link between reduction in hypotension and the rate of complications, or the long-term effects on healthcare costs and patients’ quality of life are the parameters that contribute the most to model uncertainty.

Early cost-effectiveness models are a valuable tool to inform further product development and evidence requirements, but characterization of uncertainty and transparency in modelling assumptions are key.

Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, assessment of the value of these new technologies is still unclear and no agreed international HTA-based guideline exists. Therefore, a Model for ASsessing the value of AI (MAS-AI) in medical imaging was developed by a multidisciplinary group of experts and patient representatives.

The MAS-AI guideline is based on four steps. First a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging (5,890 studies were assessed with 86 studies included in the scoping review). Next, interviews with leading researchers in AI in Denmark. The third step was two workshops where decision-makers, patient organizations and researchers discussed crucial topics when evaluating AI. Between workshops, the multidisciplinary team revised the model according to comments from workshop-participants. Last step is a validation workshop in Canada.

The MAS-AI guideline has three parts. There are two steps covering nine domains and then advises for the evaluation process. Step 1 contains a description of patients, how the AI-model was developed, and initial ethical and legal considerations. Finishing the four domains in Step 1 is a prerequisite for moving to step 2. In step 2, a multidisciplinary assessment of outcomes of the AI-application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects and patient aspects. The last part, is five advices to facilitate a good evaluation process.

We have developed an HTA based framework to support the prospective phase while introducing novel AI technologies into healthcare in medical imaging. MAS-AI can assist HTA organizations (and companies) in selecting the relevant domains and outcome measures in the assessment of AI applications. It is important to ensure uniform and valid decisions regarding the adoption of AI technology with a structured process and tool. MAS-AI can help support these decisions and provide greater transparency for all parties involved.

Artificial intelligence (AI)-supported technologies are rapidly developing and have the potential to improve healthcare quality at reduced cost. However, few examples exist of successfully deployed AI-technologies in a real-world context that have been adequately assessed. Therefore, the objective of this research is to: (i) identify existing health technology assessment (HTA) methods developed or adapted to assess AI-supported health technologies, (ii) identify new assessment topics or domains relevant for AI-technology uptake, and (iii) take the first step in developing a framework applicable for new challenges that emerge with the introduction of AI.

A systematic literature review of studies describing methods or frameworks to assess AI-supported health technologies was performed on PubMed from January 2010 until February 2021. Furthermore, a web page search of international HTA agencies and international organizations such as the World Health Organization, Organziation for Economic and Co-ordination and Development, and the European Commission was performed to identify important aspects to consider when implementing and assessing AI technologies.

No assessment frameworks for AI technologies were identified from the systematic literature review or web page searches of international HTA agencies. Reports from international organizations highlight limitations or inability of most AI technologies to ‘explain’ their decision-making process (black box issue), leading to lack of trust in the technology that affects its adoption. It is recommended to put more emphasis on assessing transparency and ‘explainability’ of the AI solution as well as aspects of safety, ethical, legal, and social issues related to implementation and the development/training phase of the AI technology.

The results from this study uncover key gaps in frameworks posed for performing a systematic and holistic assessment of AI in a real-world context of health care. However, valuable information on relevant assessment aspects for AI-supported technologies have been identified.

The results will form the basis for the development of a framework to assist decision-makers in assessing AI-supported technologies in a holistic manner for a responsible deployment – the HTA AI Framework.

Fecal incontinence (FI) is the involuntary loss of feces and can affect up to 17 percent of community dwelling individuals, rising to 40 percent of older people in residential care homes. There is limited up-to-date evidence which formally set research priorities addressing FI. This project aimed to identify research topics of highest importance to key FI stakeholders.

An evidence gap map was produced incorporating three streams of evidence coded against predefined topic domains. The evidence streams included: emerging evidence identified through horizon scanning; existing evidence identified through systematic searches of bibliographic databases; and key FI stakeholder insights collected through an international survey. Findings were presented as a visual map to facilitate knowledge exchange during an online workshop with a purposeful sample of multidisciplinary stakeholders. The identified gaps in research were explored to see whether they were deemed representative of unmet needs, and as such, areas of priority to key FI stakeholders. Ideation techniques and group discussions were used to refine and rank priority areas.

Overall, there was a mismatch between the existing and emerging evidence, and the priorities of key FI stakeholders. New pharmaceutical and medical technology innovations were limited. Eight percent of early-stage trials identified were concerned with the use of repurposed drugs. Within the existing evidence base, individual bowel management strategies and treatments were examined, however, key FI stakeholders desired interventions to improve patient education and the psychological aspects of living with FI. The five priority topics identified in order of importance are as follows: psychological support; lifestyle interventions; long-term effects; education; and constipation.

The robust methodology used to identify priority topics were successful in identifying broad and wide-ranging areas of importance to key stakeholders. The evidence gap map was a useful visual tool to facilitate knowledge exchange and highlight where research efforts have been focused historically, identifying a mismatch between the existing evidence base and what stakeholders consider important.

International collaboration on horizon scanning for medical devices is seen as desirable, because the development of medical devices is not limited to the national level, and horizon scanning for medical devices on a country-level can be challenging due to scarcity and diversity of information. The International Horizon Scanning Initiative Medical Devices Working Group (IHSI MDWG) was set up in June 2021. The objective of this study was to research the expectations and experiences of the participants of this initiative, in order to reflect on the possibilities and challenges of governing innovative medical technologies.

A questionnaire was sent out to 10 purposively selected representatives of the IHSI MDWG participating counties: Austria, Belgium, Canada, Denmark, Italy, Luxembourg, Netherlands, Norway, Portugal, and Sweden. The survey covered individual countries’ respective purposes for an international horizon scanning system as well as questions related to the desired scope and perceived challenges of such a system. The questionnaire was supplemented with online, semi-structured, in-depth interviews with the same representatives from each participating country. These interviews provided for diving deeper into the survey topics as well as discussing the relation between horizon scanning and health technology assessment, the relation to other international horizon scanning collaborations, and the relation between an international versus a national horizon scanning system. In addition, participant observations were conducted at the Dutch National Health Care Institute and during IHSI MDWG working group meetings.

Preliminary results are discussed first with participants after which we will draw our final conclusions and recommendations for practice. Our analysis focuses on exploring participants’ expectations and experiences with international horizon scanning through triangulating the three sources of data in our analysis.

The study will report on the expectations, needs and challenges of setting up an international collaboration for horizon scanning of medical devices and reflect on the regulation and governance of innovative medical technologies across several countries in Europe and Canada.

Horizon Scanning (HS) has been part of the health technology assessment (HTA) world since the end of 20th century. In accordance with the life cycle concept of heath technologies, there have been different organizations that have devoted part of their portfolio to HS’s so called Early Awareness and Alert Systems. In 2017, a legal entity international Health Tech Scan (iHTS) was created on the basis of the previous existing network EuroScan. Our aim is to describe the current achievements of the network, the methods used by its members, and their achievements.

In 2010, EuroScan decided to analyze its members’ methods and processes to perform HS. We used a previously defined questionnaire to revisit the analysis of methods, processes, and impact of the founded legal entity i-HTS. We analyzed the clients, stakeholders involved, impact on health systems and alliances, as well as the current achievements as a group.

i-HTS is currently rooted mainly in Europe and Asia-Pacific with members in the Americas and with ambassador programmes in Africa. The individual members have continued their achievements with special focus on three main aspects: proactive approach to innovators, stakeholder involvement, and client orientation. In most cases, the members of i-HTS produce information that is used for decision-making purposes, some of which influences the national or regional benefit package. Methods did not differ but the level of involvement of stakeholders in the different phases of the process. Some members also include in their portfolio early advice to innovators.

Early Awareness and Alert Systems are key to inform health care systems around technologies that could impact the management of patients in different contexts. There is a need to better understand the needs of the clients and the importance of HS in order to improve their efficiency. iHTS is in the process of redesigning its methods toolkit with the participation of all its members.

To understand the importance of the preference methods criteria to stakeholders at each decision point in the Medical Product Lifecycle (MPLC) and to determine the suitability of commonly applied preference methods (Discrete Choice Experiment [DCE], swing weighting [SW], probabilistic threshold technique [PTT], Best-Worst Scaling case 1 [BWS1], Best-Worst Scaling case 2 [BWS2]) for a given decision-point.

Nineteen preference methods criteria of an existing performance matrix were incorporated in an online survey of industry, regulatory, and health technology assessment (HTA) stakeholders. All methods criteria were given a relative weight based on the SW ranking and point allocation task in the survey. Based on this relative weight and the performance matrix values, an overall suitability score was calculated for each method per critical decision point along the MPLC. Several sensitivity analyses were conducted for which the performance matrix was adapted.

In total 59 industry, 29 regulatory, and 5 HTA representatives completed the survey. In general, ‘estimating trade-offs between characteristics’, and ‘estimating weights for treatment characteristics’ were important preference method criteria throughout all MPLC decision points, while other preference method criteria were most important only for specific MPLC stages. Both BWS1 and BWS2 seem equally suitable across decision points, DCEs seem most suitable during clinical development and regulatory launch, and SW and PTT seem most suitable throughout industry decision points. Sensitivity analysis showed substantial impact of slight changes in the performance matrix.

With rapid changes in preference research, performance matrices of preference methods should continue to be re-evaluated as more and more evidence accumulates. While DCE is the most applied preference elicitation method, other methods should also be considered to address the needs of MPLC stakeholders. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

Health Technology Reassessment (HTR) is emerging, as the focus of health technology assessment agencies shifts from traditional methods of technology adoption to managing technologies throughout their lifecycle. The National Institute for Health and Care Excellence (NICE) evaluates devices, digital and diagnostic technologies by producing medical technologies guidance, which could recommend for adoption, no adoption, or further research. The desire to move to a lifecycle approach in the evaluation of medical technologies is reflected in the guidance review process, which involves review of the technology every three years or upon notification of significant new evidence. The outcomes of the guidance review can be to amend, update, withdraw, or leave the guidance unchanged.

Information on all technologies which have undergone guidance review since the commencement of the process was collected, including the recommendation before and after review and the basis for this recommendation. The proportion of guidances which were not changed, amended, updated, and withdrawn was calculated and the trends, including the bases for recommendation change were analyzed.

In total, 34 medical technology guidance reviews have been performed. During the process, 15 (44%) were amended to reflect minor changes in the economic or clinical evidence, which did not change the recommendation. Ten (29%) were not changed, while three (9%) were updated respectively. Three (9%) were withdrawn. Another three (9%) represent special cases, which entered guidance review, but were paused due to external reasons. Among the guidances that progressed to update, two out of three had a cost increase, whereas one was broadened to reflect evidence for a larger population.

HTR is an important mechanism to improve patient care and system efficiency. In NICE’s evaluation of medical technologies, changes in the recommendation stemmed from changes in the technology’s (or standard care’s) cost, the evidence for clinical effectiveness, or the safety profile.

Due to the iterative nature of medical device innovation and development, a single once-off assessment does not provide all the answers that decision-makers need over the device’s lifetime. Consequently, a lifecycle approach is frequently recommended. However, there is no lifecycle model recognized internationally for conducting such evaluations, nor is there explicit agreement regarding what is meant by, or evaluated over, the lifecycle. The purpose of this review was to identify and explore the range of models/frameworks used for evaluating medical devices across their lifetime – to determine what people mean by ‘the lifecycle’, what is evaluated, how, and why.

A qualitative evidence synthesis of lifecycle models described in the literature from a wide variety of disciplines was performed. Literature searching and selection of models iterated with analysis. Similarities, differences, and patterns were identified, from which themes became apparent, and explanatory theories were developed.

Fifty-three models are included in the synthesis. The dimensions of difference include, amongst others, the lifecycle scope, level of application, evaluation timepoints and methods, factors included in the models, and the focus of interest. These are each influenced by the purpose of the lifecycle evaluation, which depends on the perspective and the decision or activity the evaluation is intended to inform. Few models provide a lifecycle approach to evaluating safety or efficacy. Theories explaining the differences are postulated.

Lifecycle evaluation means different things to different actors, with varied reasons for evaluation and different variables included in the models. Thus, discussions between different actors on lifecycle evaluation may be inadvertently at cross-purposes. Without first defining what is meant by the lifecycle (including the stages or phases of activity it covers) and the variables included in an evaluation, care must be exercised when discussing a lifecycle evaluation approach – to ensure that the meaning (and intended objective) is not lost in translation. Indeed, promoting lifecycle evaluation may result in necessary evidence not being generated early enough, being deferred instead until later.

Managed access approaches have been used by The National Institute for Health and Care Excellence (NICE) in partnership with NHS England since 2015. The Cancer Drugs Fund is an exemplar of this approach which enables earlier patient access to promising new treatments while further real-world data is collected to address evidential uncertainties. Increasingly, this approach is being applied to rare diseases and other conditions to address unmet clinical need with extensive involvement from patient organizations and clinicians.

All Managed Access Agreements (MAAs) in development or published in England were reviewed to present data on the number of technologies (i) entering, (ii) in active monitoring, and (iii) exiting managed access, for both cancer and other disease areas.

After six years since the first MAA (at December 2021), over 73,000 patients have benefited from earlier access to promising new treatments for cancer, genetic and rare diseases, including cystic fibrosis, spinal muscular atrophy and sickle cell disease via managed access. Fifty-eight technologies were commissioned via managed access: thirty technologies in active data collection, eleven technologies being re-evaluated, and seventeen technologies have exited managed access. Patient and clinical engagement have been essential to the successful real-world data collections delivered and underway.

Managed access is an approach for providing earlier patient access to promising new technologies that would not otherwise be recommended for use in England. The approach to managed access in England is maturing at the same time the volume of topics entering and exiting managed access in England is expected to grow throughout 2022 with the introduction of the Innovative Medicines Fund.

Prices of medicines have increasingly come under payer and societal scrutiny in many countries around the world. As the price-setting process is quite untransparent, the concept of cost-based pricing has been brought forward as an alternative method to inform reimbursement decision-making. A Real-Option rate of Return (ROroR) approach, was recently proposed as a method for a multi-stakeholder driven collaborative investment model. This study showed that there are public-private medicine development opportunities that could lead to lower research and development (R&D) costs for products with a challenging business model. The aim of the current study is to assess the practical use of the ROroR approach and highlight its sensitivity regarding input parameters.

The ROroR approach incorporates medicine-specific parameters: R&D costs, the number of patients treated per year, the time horizon for recouping the investments set by the stakeholders, the production costs and a predefined profit margin. Three hypothetical case-studies were selected for the ROroR analysis comprising of an orphan, oncology, and a more regular medicine. Parameter input data was derived from the available literature. Cost-based prices were calculated based on applying the ROroR equation under a constant profit margin. Ultimately, the corresponding prices of the case-studies were analyzed for their sensitivity using ten changes of the original value.

The ROroR approach was most sensitive to the length of the time horizon and the number of patients treated per year. The largest sensitivity was found for the oncological drug, with an asymmetric price change ranging from -25 percent to +271 percent if varying the time horizon or number of patients. The profit margin, and total R&D costs have the least effect on the price: +/-4 percent and +/-45 percent, respectively.

This study shows that cost-based pricing is highly beneficial in uncovering pricing-underlying business or economic mechanisms and suggesting a transparent price. Further research is needed on implementing public-private development models and cost-based price determination using the core parameters.

This paper reports the results of the collaboration within the European initiative of new Reimbursement and aCCess Approaches (EUreccA) which is concerned with the use of real-world evidence (RWE) in health technology assessment (HTA) decision-making. The work grew from the observation of a large, very experienced group of HTA practitioners which found that the use of RWE varied depending on the type of question asked and the particulars of the data source(s) used. We set out to examine how RWE is used in HTA decision-making and to make proposals on its facilitation.

Literature reviews covering earlier reviews of RWE use, academic papers, and HTA agency websites were combined with case studies involving interviews with decision-makers in four countries (England, France, Italy, Sweden) to identify the circumstances of breakdown of RWE use and to build a categorization of the uses of RWE and associated difficulties. This evidence supported the creation of a taxonomy of pairings of data sources and the questions they were used to address. The face validity of the approach was tested at an advisory board of senior HTA practitioners.

In total, 27 questions were identified and 10 types of data source, giving 270 pairings. These pairings were linked to relevant methods guidance and to examples of their use, itemizing HTA issues and decisions made. Reports are being prepared for publication, covering the detail of the methods of the literature searches; methods of the country case studies; a description of the taxonomy; and guidance on governance.

When using RWE in HTA decision-making, the detail of the particular data sources and question addressed matter. Recently, both the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Real-World Transparency Initiative have argued for a registry of the uses of RWE. The work described here offers a starting classification of the material that should be held in such a registry, and which in itself could be developed by the stakeholders, both agencies and companies, that use it, furthering trust and confidence.

When assessing existing or emerging technologies using a one-off health technology assessment (HTA) we do not take into consideration the effects on people who will receive the technology once approved. Developments in real-world data (RWD) can help to address this by moving to ongoing health technology management (HTM).

To move to HTM, we first need to develop HTM data requirements. We undertook user interviews with National Institute for Health and Care Excellence (NICE) HTA developers to develop a list of requirements. We surveyed the types of data that NICE currently has access to and performed a gap analysis to understand where further data is needed. We then worked with external systems partners to identify and review available data sources that could support HTM.

From our user interviews we established eight HTM data requirements. Data needed to be linked, cover full care pathways, contain data from new collections, be shareable, have direct access, be of high quality, have comprehensive coverage, and be responsive to technological developments (such as artificial intelligence). The review of data sources revealed a fragmented landscape of health data in the United Kingdom (UK). We identified National Health Service Digital’s (NHSD) Trusted Research Environment as the main data source that could address HTM requirements. This addresses challenges with fragmented data by providing approved researchers with timely and secure access to a range of linked health and care data. We also identified that a large national data collection would not capture all technologies, such as orphan technologies for rare conditions. We therefore established a process for accessing data from smaller data collections such as disease specific registries. To address how we can use this data, we developed the NICE Real-World Evidence (RWE) Framework that provides clear guidance on the expectations for the planning, conduct, reporting, and appraisal of RWE studies.

We have established requirements for the type of data that will help to deliver HTM as well as developed a process for accessing several suitable data sources that meet these requirements.

Real-world evidence (RWE) can be of value to support comparative effectiveness of drugs and technologies by providing additional information about their use for a variety of patients in real contexts of care. However, the integration of RWE in appraisals can be challenging, and INESSS felt the need to reinforce and explicit the underlying methodological and theoretical foundations.

A comprehensive literature review was carried out, followed by collaborative development work by members of the methodological and assessment teams.

The literature review led to a common understanding of RWE underlying principles and fed the subsequent phases of the project. Three factors were identified as driving the integration of RWE in reimbursement appraisals at INESSS. Specifically, (i) the design and conduct of the real-world studies are done in accordance with best practices, (ii) the results are presented transparently and include all relevant information to assess the quality of the study and the data, and (iii) the RWE submitted is appropriate and relevant for decision-making. This third component is further ascertained by considering the decisional context (what are the circumstances motivating the submission of RWE and how does it correlate or not with existing evidence?), the data (is the dataset fit for decision needs?) and the study methods (are study design and analytical methods robust enough?). Globally, INESSS considers the integration of RWE in appraisals and its weighting, in relation with the (more traditional) available evidence, to be a case-by-case exercise.

The characterization of the main factors driving the integration of RWE in reimbursement appraisals at INESSS serves as a basis for communicating the requirements for evaluation submissions by sponsors. It further reinforces INESSS capabilities in assessing innovations, which can imply an appraisal of value at various moments along the lifecycle and with a diversity of evidence types. Considering the rapidly evolving literature and international experience, this work is expected to evolve too, and will be updated as needed.