It is well documented that biomedical innovations (both pharmaceutical and non-pharmaceutical) have significantly contributed to improvement in life expectancy and quality of life over the past few decades [1]. As innovations accelerate across diseases and technology types (e.g., medicines, diagnostics), there is an increasing call for improved methods and processes in health technology assessment (HTA) frameworks to better define and measure the innovative properties of health technologies. The ISPOR Value Flower and the Second Panel are among recent examples of efforts to advance our methods in this area [2,3,4]. Such improvements will help ensure that HTA generates insights more reflective of real-world patient experience and strikes a balance among access, affordability, and long-term incentives to innovate.

This special supplement features three articles that propose novel and practical solutions to help us better define, measure, and reward innovations in HTA frameworks. These papers were selected from a call for papers as part of the Valuing Innovation Project (VIP), a multi-phase initiative launched by the Innovation and Value Initiative (IVI) and multi-stakeholder partners.

The VIP was launched by IVI with the specific aim to advance multi-stakeholder dialogues, prioritize gap areas in current HTA approaches, and identify practical methods to better measure and reward innovations [5]. In the first two phases, a targeted literature search and engagement with a multi-stakeholder expert panel yielded three prioritized attributes featured in the subsequent call for papers [6]. These attributes included real-option value, scientific spillover, and broader societal impacts of medical innovations. The three winning papers were selected in a blinded review process by a multi-stakeholder judging panel on the basis of their novelty, scientific rigor, patient centricity, and likelihood to result in practice changes in HTA.

Built on recent progress in the methods to quantify real option value (ROV) of health technologies, Li et al. proposed a “minimal modeling” approach with which value assessors could readily assess the potential scale and relevance of ROV in a specific technology assessment without constructing a full, formal cost-effectiveness model [7]. Starting from the theoretical framework of ROV, the authors discuss how HTA practitioners can leverage publicly available data sources to estimate six key parameters to calculate the potential scale of the ROV. There is uncertainty in point estimates, driven by uncertainty in data sources to derive these parameters, and the authors suggest specific ways to quantify these sources of uncertainty. The authors then discuss how ROV would likely impact costs, value estimates, and cost-effectiveness ratios in a full cost-effectiveness model through interactions with both conventional and novel elements of value. To streamline the process of reporting for HTA, a 15-item checklist was developed to facilitate the discussion of ROV (including types of ROV, size of ROV, uncertainty, interactions with conventional and novel value elements, and impact on value estimates).

To illustrate the application of the proposed approach, the authors conducted an analysis using ipilimumab for first-line treatment of metastatic melanoma at its launch in the USA in 2011. The case example extends the existing empirical analysis that focused mostly on medications with life extension benefits in oncology, as ipilimumab could also slow disease progression. The analysis shows that the ROV of ipilimumab is likely to be 10–20% of its value estimated in a conventional cost-effectiveness analysis from a narrower health system perspective. Considering uncertainty and interactions with novel elements of value, the authors estimate that the incremental cost-effectiveness ratio (ICER) would only fall under $150,000 per QALY if the future innovation were net cost-saving.

The second winning paper, by McElwee and Newall [8], examined the value of flexible vaccine manufacturing capacity, which is the ability to rapidly produce vaccines in response to emerging threats. As the authors note, additional flexibility could facilitate a more rapid pivot to production of novel vaccines during future pandemics, but the value of such flexibility would not usually be recognized using traditional HTA methods. The authors identify the key value drivers of additional flexible vaccine manufacturing capacity, develop methods for estimating its value, and discuss how this value can be recognized in HTA. They note a lack of availability of accurate and reliable tools to measure and integrate the value of flexible capacity into HTA, and outline different methods that could be used to address the value of additional flexible vaccine manufacturing capacity, including stated and revealed preference studies, macroeconomic modelling, and cost–benefit analysis.

This paper is notable for its comprehensive approach, looking at different methods and value elements that should be considered when attempting to address the value of flexible vaccine manufacturing capacity. The authors acknowledge that there is uncertainty around the value of flexible capacity and that more work is needed to reduce this uncertainty, but argue that not doing so risks underestimating the value of such capacity in preparing for the possibility of future pandemics.

Finally, in the third winning paper, Ali et al. describe a conceptual framework for estimating and rewarding the value of healthcare interventions by including effects beyond the healthcare sector [9]. The authors argue that healthcare interventions can have a positive impact on a wide range of outcomes, including economic, social, and environmental outcomes, and that these broader impacts are not usually captured in conventional HTA. They propose a novel, wide-ranging framework for capturing the value of these broader outcomes in HTA. Their framework includes three components that can be used to estimate the broader economic and societal impacts of healthcare interventions: distributional cost-effectiveness analysis (DCEA), input–output models, and a voting scheme.

This paper also includes a case study in applying the framework to a previously evaluated digital health therapeutic for opioid use disorder treatment. The case study provides an illustrative example of the practicality of applying the framework but also demonstrates that further work is needed to develop a more coherent multi-stakeholder approach to address data availability gaps. Overall, the approach described in this paper is notable partly because it includes the ability to consider equity impacts (via DCEA). It is also notable for including the opportunity for engaging with patients and other stakeholders via the voting process.

These papers represent cutting-edge thinking on how to better measure and reward innovation in healthcare. The proposed approaches pave the way forward for how HTA practitioners and value assessors can determine the importance of incorporating novel measures of innovation into a formal assessment. These methods could shed light on the true societal value of innovative treatments and better inform coverage and pricing decisions. Despite the progress introduced in these papers, it should be noted that more work will be needed to determine the feasibility of these approaches in practice, as well as to incorporate other aspects of innovation into HTA frameworks. In the meantime, we believe that these papers will make a significant contribution to the measurement of innovation in the field of health technology assessment and help to improve the decision-making process for healthcare stakeholders.