The PriTec Tool is an automatically executable multicriteria web application developed in 2009 by the Galician Health Knowledge Agency (avalia-t; ACIS) for the prioritization of technologies susceptible of post-introduction observation or obsolescence. Currently, the tool has been updated and improved to support the selection process of technologies to be assessed for inclusion into the National Public Health Care Portfolio. The aim of this work is to present the updated version of the tool (PriTec Tool 2) with the new functionalities.

The development of the tool was based on a mixed-method approach, comprising a systematic review, followed by a five-step process: (i) development of the preliminary proposal of prioritization criteria and domains; (ii) contextualization and validation of the criteria and domains by a multidisciplinary group of key stakeholders; (iii) assessment of validity, reliability and suitability of criteria; (iv) weighting of domains; and (v) evaluation of applicability, reliability and reproducibility of the tool.

The tool consists of 15 criteria categorized in 5 domains. The web application ranks the technologies through automatic computation of the weighted average of the different criteria and generates a comparative analysis of the individual or working group results. The application allows access to different options: working groups, case studies or technology comparison. It allows for individual prioritizations or managing working groups. When applied to prioritize the Spanish Network of Health Technology Assessment (HTA) Agencies yearly workplan it achieved an intraclass correlation coefficient of 0.71 (95% confidence interval 0.62, 0.88).

The updated PriTec Tool-2 can be very useful to guide decision-making regarding the assessments that would be mostly needed to ensure health, equity and sustainability. The tool stands out for its simplicity and ease of application. It is acknowledged that the tool could be of great interest to policy makers, HTA bodies and other health decision-makers worldwide.

Publication bias has the potential to adversely impact clinical decision making and patient health if alternative decisions would have been made had there been complete publication of evidence.

The objective of our analysis was to determine if earlier publication of the complete evidence on rosiglitazone’s risk of myocardial infarction (MI) would have changed clinical decision making at an earlier point in time. We tested several methods for adjustment of publication bias to assess the impact of potential time delays to identifying the MI effect. We then performed a cumulative meta-analysis (CMA) for both published studies (published-only data set) and all studies performed (comprehensive data set). We then created an adjusted data set using existing methods of adjustment for publication bias (Harbord regression, Peter’s regression, and the nonparametric trim and fill method) applied to the limited data set. Finally, we compared the time to the decision threshold for each data set using CMA.

Although published-only and comprehensive data sets did not provide notably different final summary estimates [OR = 1.4 (95 percent confidence interval [CI]: .95–2.05) and 1.42 (95 percent CI: 1.03–1.97)], the comprehensive data set reached the decision threshold 36 months earlier than the published-only data set. All three adjustment methods tested did not show a differential time to decision threshold versus the published-only data set.

Complete access to studies capturing MI risk for rosiglitazone would have led to the evidence reaching a clinically meaningful decision threshold 3 years earlier.

Systematic reviews (SRs) are central to evaluating therapies but have high costs in time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depends on performing and presenting evidence according to established best practices. In order to provide a basis for comparing between software tools that support SR, we performed a feature-by-feature comparison of SR tools.

We searched for SR tools by reviewing any such tool listed the Systematic Review Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional, and required no coding and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in four previous reviews of SR tools; we also added five features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency/replicability. Then, two reviewers assigned binary ‘present/absent’ assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements.

Of 53 SR tools found, 29 were excluded, leaving 24 for assessment. Thirty features were assessed across six classes, and the inter-observer agreement was 86 percent. DistillerSR (Evidence Partners; n = 26/30, 87%), Nested Knowledge (Nested Knowledge; n = 25/30, 83%), and EPPI-Reviewer Web (EPPI-Centre; n = 24/30, 80%) support the most features followed by Giotto Compliance (Giotto Compliance; n = 23/30, 77%), LitStream (ICF; n = 22/30, 73%), and SRDB.PRO (VTS Software; n = 21/30, 70%). Seven tools support fewer than half of all features assessed: RobotAnalyst, SyRF, Data Abstraction Assistant, SWIFT-Review, SR-Accelerator, RobotReviewer, and COVID-NMA. Notably, only 10 tools (42%) support direct search, 7 (29%) offer dual extraction, and 13 (54%) offer living/updatable reviews.

DistillerSR, EPPI-Reviewer Web, and Nested Knowledge each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.

Web-based interventions for the promotion of maternal mental health could represent a cost-effective strategy to reduce the burden associated with perinatal mental illness. This study aimed to evaluate the cost-utility of Be a Mom, a self-guided web-based cognitive behavioral therapy intervention, compared with a waiting-list control.

The economic evaluation alongside a randomized controlled trial was conducted from a societal perspective over a 14-month time frame. Postpartum women presenting low risk for postpartum depression were randomized to the intervention (n = 191) or control (n = 176) group and assessed at baseline, postintervention and 4 and 12 months after postintervention. Data regarding healthcare use, productive losses and quality-adjusted life years (QALYs) were collected and used to calculate incremental cost-effectiveness ratios (ICERs). Uncertainty was accounted for with nonparametric bootstrapping and sensitivity analyses.

At 14 months, and after accounting for a 3.5 percent discount rate, the intervention resulted in a yearly cost-saving of EUR 165.47 (−361.77, 28.51) and a QALY gain of 0.0064 (−0.0116, 0.0244). Bootstrapping results revealed a dominant ICER for the intervention group. Although results were statistically nonsignificant, cost-effectiveness acceptability curves showed that at a EUR 0 willingness to pay threshold, there is a 96 percent probability that the intervention is cost-effective when compared with the control group. The sensitivity analyses generally supported the acceptable likelihood of the intervention being more cost-effective than the control group.

From a societal perspective, the implementation of Be a Mom among low-risk postpartum women could be a cost-effective way to improve perinatal mental health.

Producing new health technology assessments (HTA) can be a time-consuming process. With finite resources in HTA agencies, limited capacities in countries without formalized HTA processes, and growing interest for lifecycles approaches valuing health technologies; innovative and efficient HTA processes are needed. “Adaptive HTA”, referring to the pragmatic use of HTA methods and existing (HTA) evidence, might offer solutions. We will present the results from a scoping review that mapped existing tools, methods, practices to transfer existing HTAs; and reflect on these findings given our own experiences of adaptation processes in LMICs.

We undertook a scoping review and systematically searched five electronic databases. Inclusion of articles followed strict in- and exclusion criteria. Data extraction focused on information regarding tools, methods, and practices that could aid the transferability of HTA analysis. Here, HTAs referred to full-HTAs and other HTA products, as partial HTAs, economic evaluations, or systematic reviews. Lastly, we mapped the possible overarching factors that can affect transferability.

The search (November 2020) identified 2030 hits, of which 19 were included. Most HTA transfers followed five steps that closely resemble a de novo HTA process. The identified transferability tools, often checklists, were merely aids or a “catalyst” for the transfer and provided limited guidance for the whole transfer process. Contrastingly, we identified three frameworks that can support the whole process: European Network for HTA (EUnetHTA) Adaptation Toolkit, TRANSFER framework for systematic reviews, and paper series on systematic reviews for economic evaluations. Lastly, our findings pointed to various challenges and knowledge gaps; especially for transfers in low and middle income countries evidence is limited.

The re-use of existing evidence in HTA reports is not new; and readily part of de novo and adaptive processes. The innovative nature of adaptive HTA comes from its ability to unpack the process of adaptation and transferability. Simultaneously, this scoping review highlighted gaps in existing adaptive methods, and could aid future adaptive HTA process for experienced and new HTA-doers.

To conduct a formative evaluation of applying the VALIDATE approach in practice by (i) assessing how students appreciated the e-learning course, (ii) exploring how, for what purposes and with what outcomes the acquired VALIDATE competences subsequently were used in internships in different institutional contexts, and how this was shaped by these contexts, and (iii) what this shows on real-world use of VALIDATE.

Comparative discussion of experiences of applying the VALIDATE approach via a semistructured survey and oral feedback from e-course students; final reports on internships in health technology assessment (HTA) practice, followed by semistructured interviews with interns and supervisors to complement and interpret results.

All students considered the VALIDATE approach an enlightening and important addition to current HTA knowledge, especially regarding understanding the relation between empirical analysis and normative inquiry, identifying policy relevant questions and using the method of reconstructing interpretive frames for scoping. The latter appeared intellectually challenging and requiring some prior HTA knowledge. The use the VALIDATE approach in practice shows that interns productively redefined the HTA problem, based on appreciation of different stakeholders’ definition of the issue; they experienced constraints from retrieving all relevant perspectives from existing literature as well as from institutional rules and routines.

Some challenges in applying the VALIDATE approach deserve attention for its future use: currently used research approaches often assume a problem as “given”; and the data needed on different perspectives is often not reported in scientific literature. Finally, data gathering on and evaluation of value dimensions was experienced as challenging.

In areas where public confidence is low and there is a lack of understanding around behaviors, such as COVID-19 vaccine hesitancy, there is a need to explore novel sources of evidence. When leveraged using artificial intelligence (AI) techniques, social media data may offer rich insights into public concerns around vaccination. Currently, sources of ‘soft-intelligence’ are underutilized by policy makers, health technology assessment (HTA) and other public health research agencies. In this work, we used an AI platform to rapidly detect and analyze key barriers to vaccine uptake from a sample of geo-located tweets.

An AI-based tool was deployed using a robust search strategy to capture tweets associated with COVID-19 vaccination, posted from users in London, United Kingdom. The tool’s algorithm automatically clustered tweets based on key topics of discussion and sentiment. Tweets contained within the 12 most populated topics with negative sentiment were extracted. The extracted tweets were mapped to one of six pre-determined themes (safety, mistrust, under-representation, complacency, ineffectiveness, and access) informed using the World Health Organization’s 3Cs vaccine hesitancy model. All collated tweets were anonymized.

We identified 91,473 tweets posted between 30 November 2020 and 15 August 2021. A sample of 913 tweets were extracted from the twelve negative topic clusters. Of these, 302 tweets were coded to a vaccine hesitancy theme. ‘Safety’ (29%) and ‘mistrust’ (23%) were the most commonly coded themes; the least commonly coded was ‘under-representation’ (3%). Within the main themes, adverse reactions, inadequate assessment, and rushed development of the vaccines as key findings. Our analysis also revealed widespread sharing of misinformation.

Using an AI-based text analytics tool, we were able to rapidly assess public confidence in COVID-19 vaccination and identify key barriers to uptake from a corpus of geo-located tweets. Our findings support a growing body of evidence and confidence surrounding the use of AI tools to efficiently analyze early sources of soft-intelligence evidence in public health research.

Our aim was to assess the value of nintedanib for non-idiopathic progressive fibrosing interstitial lung disease (non-IPF PF-ILD) and systemic sclerosis-associated ILD (SSc-ILD) in the Spanish context, using a multi-criteria decision analysis (MCDA).

Following an adaptation of the Evidence and Value: Impact on DEcision Making (EVIDEM) MCDA methodology, the estimated value of nintedanib was obtained by means of an additive linear model that combined individual weights (100-points distribution) of criteria with the individual scoring of nintedanib in each criterion for every indication, assigned by a multidisciplinary committee of twelve clinicians, patients, pharmacists, and decision-makers. To assess the reproducibility, an alternative weighting method was applied, as well as a re-test of weights and scores at a different moment of time.

The experts committee recognized nintedanib as an intervention with a positive value contribution in comparison to placebo for the treatment of non-IPF PF-ILD (0.50 ± 0.16, on a scale from −1 to 1) and SSc-ILD (0.40 ± 0.12), diseases which were considered as very severe and with high unmet needs. The drug was perceived as a treatment that provides an added therapeutic benefit for patients (0.06–0.07), given its proven clinical efficacy (0.05–0.06), slight improvements in patient-reported outcomes (0.01–0.02), and similar safety profile than placebo (−0.04–0.00), which will likely be positioned as a recommended therapy in the next clinical practice guidelines updates.

Under this increasingly used methodology, nintedanib has shown to provide a positive value estimate for non-IPF PF-ILD and SSc-ILD when compared to placebo in Spain.

The COVID-19 pandemic has highlighted the need for rapid assessment of potential health technologies that can improve health outcomes in COVID-19 patients, as well as helping pressurized health service provision. Medical technologies play a key role in the COVID-19 pandemic, especially diagnostic tests and respiratory technologies. This study evaluates the rapid response work that the medical technology evaluation programme (MTEP) at the National Institute for Health and Care Excellence (NICE) has done in response to the COVID-19 pandemic.

Companies routinely submit medical technologies for evaluation by NICE through HealthTech Connect, which is an online portal for devices, diagnostics and digital technologies intended for use in the NHS or wider United Kingdom health and care system. During the COVID-19 pandemic, companies were able to use a designated email address if they perceived their technology may benefit the healthcare system regarding the COVID-19 pandemic. This new system bypassed the usual full registration and data submission. All technologies were reviewed that were submitted via HealthTech connect and email between March 2020 and June 2021.

During this period, 20 technologies were submitted to MTEP. Most of these technologies were submitted via email. These technologies consisted of a mix of digital, diagnostic, and respiratory technologies. Seven technologies were selected for a rapid COVID-19 MedTech innovation briefing (MIB), with one specifically addressing issues around waiting lists because of knock-on effects of COVID-19 restricting normal clinical work. A further six technologies were not selected because of limited evidence, while one was not selected because it was not perceived as innovative. The other five technologies were progressed as normal MIBs as there was not enough evidence of potential benefits related to COVID-19 to expedite to a rapid COVID-19 MIB. In total, two technologies were selected for medical technology guidance (myCOPD and Anaconda) and are currently in development.

MTEP has responded to the COVID-19 pandemic by prioritising and producing rapid COVID-19 MIBs on technologies to improve health and social care.

To investigate the test–retest reliability of the Costs for Patients Questionnaire (CoPaQ).

Through an online survey, individuals were invited to participate in a two-step study to assess the test–retest reliability of the CoPaQ. Participants to the first step were invited to complete the questionnaire a second time 2 weeks after. Reliability was assessed by calculating Cohen’s Kappa coefficients and intraclass correlation coefficients (ICC) for discrete and continuous data, respectively. A sensitivity analysis was carried out.

From a total of 1,200 participants who completed the first test, 403 completed the second test. The ICC varied from −0.00 to 0.98 with poor, moderate, good, and excellent results. The Kappa coefficients varied from −0.004 to 0.65 and were poor, slight, fair, moderate, and substantial. The sensitivity analysis showed the median value of ICC and Kappa coefficients for each category before and after the outliers’ exclusion. The median value of ICC changed from 0.30 (before) to 0.70 (after), and from 0.12 (before) to 0.04 (after), respectively, for each category. The median value of the Cohen’s Kappa coefficient increased from 0.44 (before) to 0.46 (after) and decreased from 0.32 (before) to 0.30 (after), respectively.

Test–retest reliability results indicated that the CoPaQ has a moderate reliability in terms of ICC and Kappa coefficients. The moderate reliability observed gives additional support for the applicability of this tool in economic evaluations of health interventions. Additional studies including on other properties and a cultural adaptation could further enhance the use of the tool.

A source of debate among the health technology assessment (HTA) community is what perspective should be taken in health economic evaluations. Many stakeholders advocate that a societal perspective is taken in order to include a comprehensive range of costs and outcomes and (in theory) make societally optimal decisions. The Second Panel on Cost-Effectiveness in Health and Medicine recommended that a societal perspective be presented alongside a health sector one. The Second Panel included environment as one item on its impact inventory—alongside productivity, education, and others—intended to support the use of a societal perspective. However, many HTA agencies, including the National Institute for Health and Care Excellence (NICE), have continued to use health sector-specific evaluations to inform decision-making. The presentation seeks to examine whether consideration of the environmental impact of healthcare requires/implies the formal adoption of a societal perspective in health economic analyses.

The presentation will provide an overview of the societal perspective, explaining how it differs from a health sector perspective and describing its main strengths and weaknesses. We then present policy analysis undertaken by NICE’s Science Policy and Research team to identify reasons for measuring environmental impact in HTAs and examine whether these align with the broader arguments for or against adopting a societal perspective in economic analyses.

Three reasons for considering environmental impact are identified: (i) to support parallel policies which demand healthcare system transformation against emissions targets; (ii) to ensure planetary and human health, in the future as well as the present; and (iii) to offset future healthcare resource use. We show that only the third reason aligns with arguments related to the choice of perspective for economic analyses. Moreover, this reason is arguably better aligned to maintaining a (potentially modified) health sector perspective. The implications of the results will be discussed with reference to updating reimbursement decision-making frameworks, such as those used by NICE, to account for the environmental consequences of healthcare.

To estimate the minimum percent change in failed extubation to make a tool designed to reduce extubation failure (Extubation Advisor [EA]) economically viable.

We conducted an early return on investment (ROI) analysis using data from intubated intensive care unit (ICU) patients at a large Canadian tertiary care hospital. We obtained input parameters from the hospital database and published literature. We ran generalized linear models to estimate the attributable length of stay, total hospital cost, and time to subsequent extubation attempt following failure. We developed a Markov model to estimate the expected ROI and performed probabilistic sensitivity analyses to assess the robustness of findings. Costs were presented in 2020 Canadian dollars (C$).

The model estimated a 1 percent reduction in failed extubation could save the hospital C$289 per intubated patient (95 percent CI: 197, 459). A large center seeing 2,500 intubated ICU patients per year could save C$723,124/year/percent reduction in failed extubation. At the current annual price of C$164,221, the EA tool must reduce extubation failure by at least 0.24 percent (95 percent CI: .14, .41) to make the tool cost-effective at our site.

Clinical decision-support tools like the EA may play an important role in reducing healthcare costs by reducing the rate of extubation failure, a costly event in the ICU.

In light of government and healthcare system commitments to reducing the carbon footprint of healthcare, health technology assessment (HTA) agencies are increasingly motivated to investigate how to consider environmental sustainability in their assessments and guidance. This constitutes a major departure from the existing remits and objectives of most agencies, which typically focus on improving population health outcomes. This presentation seeks to identify options for incorporating environmental impact data into HTA and to examine the main challenges, focusing on the National Institute for Health and Care Excellence (NICE) as a case study.

We present four broad approaches that could be pursued, informed by policy analysis undertaken by NICE. The strengths, weaknesses and implications of each approach are assessed.

The first option is to act as an ‘information conduit’, aggregating and distributing in a standardized format environmental impact information that is provided voluntarily by health technology manufacturers. The second is to present complementary analyses of environmental impact data, separately but alongside results from established health economic analyses (‘parallel evaluation’ model). The third is to incorporate environmental impact data into health economic analyses, for example by monetizing environmental outcomes, so that quantitative estimates of treatment value are directly affected by environmental benefits and costs (‘integrated evaluation’ model). The fourth is to create new decision-making frameworks for evaluating healthcare interventions that are not expected to improve health-related outcomes, but claim to have relative environmental benefits.

We conclude that these approaches are not mutually exclusive, and all involve some degree of benefit and risk. We explain why the parallel evaluation model may be the most appropriate approach for NICE as a first response to the increased demand for guidance on the environmental impact of health technologies. We also outline activities being undertaken by NICE and other agencies such as the Canadian Agency for Drugs and Technologies in Health to develop new methodologies for incorporating environmental impact data into their HTAs.

There has been a lack of health technology assessment (HTA) methods for novel digital health technologies (DHTs) such as mHealth, artificial intelligence, and robotics in Finland. The Digi-HTA method has been developed for this purpose. The aim of this study is to determine whether it would be possible to use Digi-HTA recommendations to support healthcare decision-makers. Secondly, from the perspective of companies offering different types of DHT products, this study assesses the suitability of using the Digi-HTA framework to perform HTAs for their products.

Feedback about Digi-HTA recommendations was collected from healthcare professionals. DHT companies provided input about the Digi-HTA framework. Data were collected via a web-based survey and were analyzed using qualitative methods.

Of the twenty-four healthcare professional respondents, twenty said that the Digi-HTA recommendations contained all the necessary information, and twenty-one found them useful for their work. Respondents hoped that the Digi-HTA recommendations would be better integrated into the decision-making processes and healthcare professionals would be more informed about this new HTA process. The questions of the Digi-HTA framework were applicable for different DHT products based on the responses from DHT companies (n = 8).

According to the study participants, although the Digi-HTA recommendations include clear and beneficial information, their integration into healthcare decision-making processes should be improved. Responses from DHT companies indicate that the Digi-HTA framework would be an appropriate tool for performing assessments for their products. To generalize the findings of this study, more comprehensive studies will be needed.

Climate change as the result of human action and the risks it poses to human health are well established. In healthcare there is increasing attention to climate and environmental impacts of the use of medical and health technologies. As part of a lifecycle approach, health technology assessment (HTA) needs to take climate and environmental impacts into account. In 2020, the new definition of HTA added the dimension ‘environmental aspects’, with which the value of health technologies can be determined and assessed in terms of their impact on the environment. This led several HTA organizations to explore opportunities for including environmental impacts in HTA procedures. It is, however, yet unclear how many researchers and HTA organizations are already working on this sustainability dimension, in what way, with which (international) partners, and what they have achieved as of now. Furthermore, the complex relations between the climate crisis, environmental pollution, health and care are difficult to trace, and methods are scarce. In HTA, there is an increasing need for outcome measures that, in addition to clinical utility, effectiveness, efficiency or satisfaction, also quantify the environmental impact of medical interventions (i.e., green metrics).

We report on (i) a scoping of international (research) groups and (HTA) organizations that are working on green metrics; (ii) a literature review into the state of affairs with regard to metrics and methods; and (iii) an impact analysis of possible future inclusion of green metrics in HTA procedures. We supplemented a review of (grey) literature with interviews with HTA organizations pioneering with green metrics, and we have conducted a review of available scientific literature, yielding examples of incorporation of environmental aspects into HTA and reports on practical implications.

Carbon dioxide emissions and pollution by the health sector are currently being explored as green metrics. Differences between direct and indirect environmental impacts complicate the evaluation.

Green metrics should eventually make it possible to assess sustainability in healthcare as part of a lifecycle approach.

Disinvestment from low value health technologies is growing globally. Diverse evidence gathering and assessment methods were used to implement disinvestment initiatives, however, less than half of the empirical studies report reduced use of the low-value services. This scoping review aimed to synthesize the information from available reviews on the concepts and purposes of disinvestment in healthcare, the approaches and methods used, the role of stakeholders and facilitators and barriers in its implementation.

This scoping review was guided by the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA statement for scoping review. Published reviews on disinvestment were identified from scientific databases including health technology assessment (HTA) Web sites using the terms “disinvestment,” “health technology reassessment,” and “healthcare.” The data obtained was synthesized narratively to identify similarities and differences across the approaches based on the prespecified categories.

Seventeen reviews were included with thirty-four initiatives identified across sixteen countries at various levels of implementation and responsible agencies for the activities. Two most used methods to facilitate disinvestment decisions are Programme Budgeting and Marginal Analysis (PBMA) and HTA. Stakeholder involvement is the most important aspect to be addressed, as it acts as both facilitator and barrier in disinvestment initiatives implementation.

Disinvestment programs have been implemented at multilevel, involving multistakeholders and using multiple methods such as PBMA and HTA. However, there is a lack of clarity on the additional dimensions of technical analysis related to these tools. Further research could focus on technology optimization in healthcare as part of overall health technology management.

At the United Nations Climate Change Conference (COP26) in 2021, over 40 countries made commitments to low carbon, sustainable health care. Respiratory care provides a case study to explore how existing evidenced-based guidance can inform progress towards more sustainable care pathways and technologies. Our aim is to identify whether environmental aspects of health technology assessment (HTA) are referenced in guidance and the extent to which the four principles of sustainable health care (prevention, self-care, streamlining, and low carbon technology) are promoted in guidance.

Internet searches enabled identification of current national guidance on management of respiratory diseases in English, French or Polish. Guidances were reviewed to identify references to environmental aspects of HTA and recommendations that align with each of the four sustainable healthcare principles.

Guidance on respiratory care is produced by varied stakeholders globally. Some principles of sustainable health care are frequently reflected in guidance to improve quality of care, but others are missed where environment sustainability is not considered. Reference to HTAs incorporating environmental impacts is lacking. There is limited engagement with the environmental impacts of inhalers in guidance. Guidance created by clinician groups (e.g., Greener Practice) and research networks (e.g., Centre for Sustainable Health Systems) has responded more quickly to the need to address sustainability concerns compared to guidance produced by national public bodies.

HTA organizations may need to take a broader perspective, incorporating environmental impacts in assessments. This could have an influential role in enabling evidence-informed guidance and development of sustainable care pathways and technologies. Limitations of our study were lack of evaluation of local guidance due to limited capacity, language restrictions, and subjectivity in assessing whether each sustainable healthcare principle was addressed in guidance. There may be limited transferability of our results to other specialties or settings. Further research on the sustainability impacts and relative merits of different health technologies and care pathways is required to inform HTA and guidance.

Patients are increasingly involved in the decision-making process for health technology assessment (HTA), but the question of at what stage they can be involved is still controversial. In Kazakhstan, the HTA process began in 2010. Over the past 2 years, implementation of a project to develop a priority-setting tool based on evidence-informed deliberative processes has made it possible to discuss the participation of patients in HTA. We explored the possibilities of participation of patients or a patient-oriented group in the HTA process.

Structured interviews were held with eight people with interests in HTA. Two were representatives of universities, two from a patient-oriented group, two experts in HTA, and two physicians. Interviews were held online by mobile phone or Zoom for 25–30 minutes. Question structures were formed based on the report ‘Patient Involvement in Health Technology Assessment in Europe 2010’. Seven stages were considered.

All participants partially or completely agreed with the involvement of patients at the HTA stages of identification and prioritization. One or two did not agree with their involvement at the HTA assessment, information production, internal and external review, and diffusion and dissemination stages. Challenges for patient involvement in HTA can be related to other commitments for patients and their carers, lack of financial affordability, conflict of interest, and lack of capacity of the HTA agency to involve them. Five participants agreed on challenges for patients to being meaningfully involved in decision-making on health technologies. These included understanding which institution makes the decisions, finding an interlocutor within the decision-making body, and understanding the decision-making process. Other issues were technical and language difficulties, lack of commitment from decision-makers and the legal or policy framework for patient involvement in HTA decision-making.

Patients can participate in HTA, but the HTA agency must first prepare and agree on the level of patient participation, and develop measures to reduce barriers such as language difficulties, and patient obligations.

To clarify the concept of disruptive technologies in health care, provide examples and consider implications of potentially disruptive technologies for health technology assessment (HTA).

We conducted a systematic review of conceptual and empirical papers on healthcare technologies that are described as “disruptive.” We searched MEDLINE and Embase from 2013 to April 2019 (updated in December 2021). Data extraction was done in duplicate by pairs of reviewers utilizing a data extraction form. A qualitative data analysis was undertaken based on an analytic framework for analysis of the concept and examples. Key arguments and a number of potential predictors of disruptive technologies were derived and implications for HTA organizations were discussed.

Of 4,107 records, 28 were included in the review. Most of the papers included conceptual discussions and business models for disruptive technologies; only few papers presented empirical evidence. The majority of the evidence is related to the US healthcare system. Key arguments for describing a technology as disruptive include improvement of outcomes for patients, improved access to health care, reduction of costs and better affordability, shift in responsibilities between providers, and change in the organization of health care. A number of possible predictors for disruption were identified to distinguish these from “sustaining” innovations.

Since truly disruptive technologies could radically change technology uptake and may modify provision of care patterns or treatment paths, they require a thorough evaluation of the consequences of using these technologies, including economic and organizational impact assessment and careful monitoring.