Innovative health technologies offer much to patients, clinicians, and health systems. Policy makers can, however, be slow to embrace innovation for many reasons, including a less robust body of evidence, perceived high costs, and a fear that once technologies enter the health system, they will be difficult to remove. Health technology funding decisions are usually made after a rigorous health technology assessment (HTA) process, including a cost analysis. However, by focusing on therapeutic value and cost-savings, the traditional HTA framework often fails to capture innovation in the assessment process. How HTA defines, evaluates, and values innovation is currently inconsistent, and it is generally agreed that by explicitly defining innovation would recognize and reward and, in turn, stimulate, encourage, and incentivize future innovation in the system. To foster innovation in health technology, policy needs to be innovative and utilize other HTA tools to inform decision making including horizon scanning, multicriteria decision analysis, and funding mechanisms such as managed agreements and coverage with evidence development. When properly supported and incentivized, and by shifting the focus from cost to investment, innovation in health technology such as genomics, point-of-care testing, and digital health may deliver better patient outcomes. Industry and agency members of the Health Technology Assessment International Asia Policy Forum (APF) met in Taiwan in November 2023 to discuss the potential of HTA to foster innovation, especially in the Asia region. Discussions and presentations during the 2023 APF were informed by a background paper, which forms the basis of this paper.

Effective policymaking in health care systems begins with a clear typology of the terminology – need, demand, supply and access to care – and their interrelationships. However, the terms are contested and their meaning is rarely stated explicitly. This paper offers working definitions of need, demand and supply. We draw on the international literature and use a Venn diagram to explain the terms. We then define access to care, reviewing alternative and competing definitions from the literature. We conclude by discussing potential applications of our conceptual framework to help to understand the interrelationships and trade-offs between need, demand, supply and access in health care.

Objectives: To compare four contingent valuation elicitation methods as a means to estimate the value of a pneumococcal vaccine in Bangladesh and to test if the elicitation methods are subject to bias and if they produce valid responses.

Methods: Three hundred sixty-one households with at least one child under 5 years of age were recruited in Dhaka, Bangladesh. Subjects were cluster-randomized to various elicitation methods: open-ended, dichotomous choice (at one of two asking prices), payment card (one of two cards with differing ranges). The dichotomous choice method was then followed up with a bidding game methodology, with the dichotomous choice price acting as the starting price for the bidding game. Analysis focused on summary statistics, demand curve estimation and multivariate regression analysis to test for validity and bias.

Results: Thirty-one households refused to participate, leaving a total of 330 participating households (a 91.4 percent response rate). Willingness to pay estimates varied significantly across the methods (p < .001), with average estimates varying between $2.34 and $18 (US). The open-ended elicitation method was found to produce highly inflated values that were insensitive to construct validity tests. The dichotomous choice method produced quantity (demand) estimates rather than value estimates, and there was some evidence of yea saying. The payment card elicitation method was found to be affected by range bias. The bidding game elicitation method was found to be less sensitive to starting point bias and yea-saying.

Conclusions: Different elicitation format do give rise to different demand curves; however, this may be partially due to the fact that they do not measure the same outcome. For example, the dichotomous choice format produces a demand curve, while the payment card, open-ended and bidding game produce inverse demand curves. All formats are prone to multiple biases. When choosing an elicitation format, it is important to first consider the purpose and use of the data. Each elicitation method has strengths and weaknesses and can be used for different purposes in technology assessment.