Dealing With Uncertainty in Early Health Technology Assessment: An Exploration of Methods for Decision Making Under...

Area of Science:

• Health economics
• Decision analysis
• Medical innovation evaluation

Background:

• Early health economic modeling (EHEM) faces challenges due to unknown consequences of innovations.
• Decision making under deep uncertainty utilizes exploratory modeling (EM) when models and probabilities are unclear.
• EM is suited for situations with unknown system models, input probabilities, and consequences.

Purpose of the Study:

• To evaluate the application of exploratory modeling (EM) for the early assessment of health technologies.
• To compare EM with EHEM in the context of early health technology evaluation.
• To identify the differences, merits, and drawbacks of using EM in this setting.

Main Methods:

• Applied exploratory modeling (EM) and early health economic modeling (EHEM) to evaluate minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage.
• Compared the outcomes derived from both EM and EHEM approaches.
• Analyzed the distinct ways each model handles uncertainty.

Main Results:

• EHEM focuses on technology value while accounting for uncertainty; EM prioritizes understanding and navigating uncertainty.
• EM aims to identify robust strategies that perform well across diverse plausible futures.
• In the case study, EM identified a policy for severe intracerebral hemorrhage patients as most robust, irrespective of MIS specifics.

Conclusions:

• EM and EHEM are complementary tools for health technology assessment.
• EM is ideal for exploring uncertainty and multiple strategies in early innovation phases.
• EHEM is better for optimizing established strategies, but EM's complexity requires stakeholder willingness for flexibility.