OP07 Dealing With Uncertainty In Early Health Technology Assessment: An Exploration Of Methods For Decision-Making Under Deep Uncertainty. (23rd December 2022)

• Record Type: Journal Article
• Authors: Scholte, Mirre; Marchau, Vincent; Kwakkel, Jan; Rovers, Maroeska; Grutters, Janneke
• Is Part Of: International journal of technology assessment in health care. Volume 38(2022)Supplement 1
• Page Start: S3
• Page End: S4
• Publication Date: 2022-12-23
• Subjects: Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S0266462322000678
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : Introduction: In early stages, the consequences of innovations are often unknown or deeply uncertain. This complicates health economic modelling. The field of decision-making under deep uncertainty (DMDU) uses exploratory modelling (EM) to help decision-makers make sound decisions under conditions of deep uncertainty (i.e., when stakeholders do not know, or cannot agree on, the system model, the probability distributions to place over the inputs to these models, which consequences to consider, and their relative importance). The aim of this research was to evaluate the potential of EM for the early evaluation of health technologies. Methods: EM and early health economic modelling (EHEM) were applied to an early evaluation of minimally invasive surgery (MIS) for acute intracerebral hemorrhage, and were compared to derive differences, merits, and drawbacks of EM. Results: The approaches fundamentally differ in the way uncertainty is handled. Where in EHEM the focus is on the value of the technology, while accounting for the uncertainty, EM focuses on the uncertainty. EHEM aims to assess whether the innovative strategy is potentially cost-effective, often using assumptions. EM on the other hand focuses on finding robust strategies (i.e., strategies that give relatively good outcomes over a wide range of plausible futures). This was also reflected in our case study. For example, EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a strategy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs. Conclusions: EM seems most suited in the very early phases of innovation (i.e., when a problem is signaled). Here, it can explore the robustness of many potential strategies under uncertainty. When potential strategies are selected, EHEM seems useful to optimize these strategies. Yet, EM methods are complex and might only be fully effective when a policy window exists that facilitates flexible research and adoption strategies.