Early Health Economic Modelling in Innovative Health Technologies: A Framework for Early Decision-Making

• Early health economic modelling (EHEM) differs from HTA modelling • EHEM informs early-stage innovation development decisions • EHEM clarifies value drivers and positioning • EHEM identifies performance thresholds and use cases • EHEM informs structured decision-making under uncertainty Early health economic modelling (EHEM) assesses value, feasibility, and evidence needs during early development of diagnostics, medical devices, and digital health technologies. This manuscript clarifies the role of EHEM, synthesizes lessons from applied work, and provides practical guidance for manufacturers and investors. We conducted a structured narrative review of the literature on EHEM, drawing on conceptual frameworks, reviews, and applied studies across diagnostics, devices, and digital health technologies to clarify what EHEM is, how it differs from traditional health technology assessment, and how it is used in practice. We synthesized recurring themes regarding value drivers, uncertainty, and strategic decision-making and distilled these into practical recommendations for development. EHEM uses flexible, fit-for-purpose decision-analytic approaches to integrate early technical data, small clinical studies, expert elicitation, and analogous evidence. Across applied cases, EHEM clarifies where value is created, identifies key performance and adoption thresholds, supports product–market alignment, and reveals priority evidence needs. When used iteratively, EHEM can help clarify investment risks and strengthen communication with payers, regulators, and other stakeholders. Practical guidance emphasizes starting modelling as early as possible, focusing on unmet need and value dimensions, engaging senior cross-disciplinary expertise, and treating models as living tools throughout development. In an era of increasing cost pressure and resource constraints in health systems, EHEM should be increasingly considered a core strategic capability for efficient, value-oriented innovation. Manufacturers and investors may benefit from integrating EHEM routinely into their early R&D processes to ensure promising technologies advance with credible, well-aligned value propositions.

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Publication Details

Journal
Health Policy and Technology
Published
2026-03-01
DOI
https://doi.org/10.1016/j.hlpt.2026.101203
Primary Topic
Health Systems, Economic Evaluations, Quality of Life
Type
article
Field-Weighted Citation Impact
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article

Early Health Economic Modelling in Innovative Health Technologies: A Framework for Early Decision-Making

Bjoern Schwander, Julie Frappier, York Zöllner
Health Policy and Technology
Health Systems, Economic Evaluations, Quality of Life
article

Early Health Economic Modelling in Innovative Health Technologies: A Framework for Early Decision-Making

Bjoern Schwander, Julie Frappier, York Zöllner
article en

Abstract

• Early health economic modelling (EHEM) differs from HTA modelling • EHEM informs early-stage innovation development decisions • EHEM clarifies value drivers and positioning • EHEM identifies performance thresholds and use cases • EHEM informs structured decision-making under uncertainty Early health economic modelling (EHEM) assesses value, feasibility, and evidence needs during early development of diagnostics, medical devices, and digital health technologies. This manuscript clarifies the role of EHEM, synthesizes lessons from applied work, and provides practical guidance for manufacturers and investors. We conducted a structured narrative review of the literature on EHEM, drawing on conceptual frameworks, reviews, and applied studies across diagnostics, devices, and digital health technologies to clarify what EHEM is, how it differs from traditional health technology assessment, and how it is used in practice. We synthesized recurring themes regarding value drivers, uncertainty, and strategic decision-making and distilled these into practical recommendations for development. EHEM uses flexible, fit-for-purpose decision-analytic approaches to integrate early technical data, small clinical studies, expert elicitation, and analogous evidence. Across applied cases, EHEM clarifies where value is created, identifies key performance and adoption thresholds, supports product–market alignment, and reveals priority evidence needs. When used iteratively, EHEM can help clarify investment risks and strengthen communication with payers, regulators, and other stakeholders. Practical guidance emphasizes starting modelling as early as possible, focusing on unmet need and value dimensions, engaging senior cross-disciplinary expertise, and treating models as living tools throughout development. In an era of increasing cost pressure and resource constraints in health systems, EHEM should be increasingly considered a core strategic capability for efficient, value-oriented innovation. Manufacturers and investors may benefit from integrating EHEM routinely into their early R&D processes to ensure promising technologies advance with credible, well-aligned value propositions.

Health Policy and Technology
Universität Hamburg (DE), Fachverband Gebäude-Klima (Germany) (DE), Action Network (US), HAW Hamburg (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Health Systems, Economic Evaluations, Quality of Life
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