Decision making under deep uncertainty for pandemic policy planning.

Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries).

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

Objectives: In early stages, the consequences of innovations are often unknown or deeply uncertain, which complicates early health economic modeling (EHEM). The field of decision making under deep uncertainty uses exploratory modeling (EM) in situations when the system model, input probabilities/distributions, and consequences are unknown or debated. Our aim was to evaluate the use of EM for early evaluation of health technologies.

Not a crystal ball: Mapping opportunities and threats for the future demand of red blood cells in the Netherlands using a scenario approach.

Background: As Western blood transfusion practices are changing, there is interest and need in anticipating the future demand of blood products and how a blood establishment can actively prepare for various long-term developments. This article provides an overview of how a scenario approach was used to prioritize key categories of drivers for the future demand of red blood cells and the organizational implications thereof for Sanquin, the Dutch national blood establishment.

Study Design And Methods: Based on previously identified drivers from interviews and a literature review (Step 1), we conducted scenario sessions and a survey to rank a list of drivers ("themes") with its related opportunities and threats (Step 2), to identify mitigating measures per theme through focus groups (Step 3).