DECISION-RELEVANT EARLY HEALTH ECONOMIC MODELLING TO INFORM DEVELOPMENT AND INVESTMENT IN EMERGING HEALTH TECHNOLOGIES
Author(s)
Bjoern Schwander, BSc, MA, RN, PhD1, Julie Frappier, BS, MSc2, York Francis Zoellner, PhD3;
1AHEAD GmbH, General Manager & Founder, Bietigheim-Bissingen, Germany, 2TOWWERS Institute (Data 4 Actions), Montreal, QC, Canada, 3Hamburg University of Applied Sciences, Hamburg, Germany
1AHEAD GmbH, General Manager & Founder, Bietigheim-Bissingen, Germany, 2TOWWERS Institute (Data 4 Actions), Montreal, QC, Canada, 3Hamburg University of Applied Sciences, Hamburg, Germany
OBJECTIVES: To examine how early health economic modelling (EHEM) can support development and investment decision-making for diagnostics, medical devices, and digital health technologies (DMDD), and to identify recurring decision-relevant insights generated through early modelling under conditions of high uncertainty.
METHODS: We synthesized evidence from published EHEM frameworks, applied case studies, and stakeholder-oriented analyses in the DMDD space. The focus was on how early decision-analytic models integrate sparse technical, clinical, and operational evidence to explore potential value under uncertainty. We assessed the types of outputs generated by EHEM that inform early strategic decisions, including value drivers, performance thresholds, scenario analyses, and priority levelling of evidence needs.
RESULTS: Across technology domains, early modelling consistently supported five decision-critical functions. First, EHEM clarified where value is generated within clinical and operational pathways. Second, threshold and headroom analyses identified minimum performance, uptake, or cost conditions required for viability. Third, early modelling refined product-market alignment by highlighting high-value indications, populations, and use cases. Fourth, EHEM identified the uncertainties that most strongly influence value, enabling more targeted and efficient evidence generation. Fifth, scenario-based analyses reduced commercial and investment risk by identifying development paths that were unlikely to succeed under plausible assumptions. Iterative use of EHEM could show to improve communication between manufacturers, investors, and other stakeholders by making assumptions, trade-offs, and value propositions explicit at an early stage.
CONCLUSIONS: Early health economic modelling is an effective strategic decision-support tool for diagnostics, medical devices, and digital health technologies. When applied early and iteratively, EHEM can support capital-efficient development, improve product-market alignment, and increase the likelihood for emerging technologies to advance with credible, decision-relevant value propositions. By generating transparent, decision-relevant value signals at the point where key development and investment decisions are made, EHEM directly addresses ISPOR’s 2030 priority of improving the relevance, timeliness, and usability of HEOR for real-world decision-makers.
METHODS: We synthesized evidence from published EHEM frameworks, applied case studies, and stakeholder-oriented analyses in the DMDD space. The focus was on how early decision-analytic models integrate sparse technical, clinical, and operational evidence to explore potential value under uncertainty. We assessed the types of outputs generated by EHEM that inform early strategic decisions, including value drivers, performance thresholds, scenario analyses, and priority levelling of evidence needs.
RESULTS: Across technology domains, early modelling consistently supported five decision-critical functions. First, EHEM clarified where value is generated within clinical and operational pathways. Second, threshold and headroom analyses identified minimum performance, uptake, or cost conditions required for viability. Third, early modelling refined product-market alignment by highlighting high-value indications, populations, and use cases. Fourth, EHEM identified the uncertainties that most strongly influence value, enabling more targeted and efficient evidence generation. Fifth, scenario-based analyses reduced commercial and investment risk by identifying development paths that were unlikely to succeed under plausible assumptions. Iterative use of EHEM could show to improve communication between manufacturers, investors, and other stakeholders by making assumptions, trade-offs, and value propositions explicit at an early stage.
CONCLUSIONS: Early health economic modelling is an effective strategic decision-support tool for diagnostics, medical devices, and digital health technologies. When applied early and iteratively, EHEM can support capital-efficient development, improve product-market alignment, and increase the likelihood for emerging technologies to advance with credible, decision-relevant value propositions. By generating transparent, decision-relevant value signals at the point where key development and investment decisions are made, EHEM directly addresses ISPOR’s 2030 priority of improving the relevance, timeliness, and usability of HEOR for real-world decision-makers.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
P51
Topic
Health Technology Assessment
Topic Subcategory
Decision & Deliberative Processes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas