RESUMO
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. METHODS: We applied EM and EHEM to an early evaluation of minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage and compared these models to derive differences, merits, and drawbacks of EM. RESULTS: EHEM and EM differ fundamentally in how uncertainty is handled. Where in EHEM the focus is on the value of technology, while accounting for the uncertainty, EM focuses on the uncertainty. EM aims to find robust strategies, which give relatively good outcomes over a wide range of plausible futures. This was reflected in our case study. EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a policy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs. CONCLUSIONS: EHEM and EM were found to complement each other. EM seems most suited in the very early phases of innovation to explore existing uncertainty and many potential strategies. EHEM seems most useful to optimize promising strategies, yet EM methods are complex and might only add value when stakeholders are willing to consider multiple solutions to a problem and adopt flexible research and adoption strategies.
Assuntos
Endoscopia , Avaliação da Tecnologia Biomédica , Humanos , Incerteza , Análise Custo-Benefício , Tomada de DecisõesRESUMO
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). RESULTS: In Step 2, 10 themes were found that were classified in terms of importance and uncertainty. These were plotted on a two-dimensional graph with an ellipse to indicate the interquartile ranges per theme. Experts rated the top three most important themes to be the blood supply organization, precision medicine, and red blood cell replacements. In Step 3, focus groups identified specific mitigating measures per theme. These measures had parallel ideas, such as the need for an innovative mentality, internal and external communication and collaboration, and building Sanquin's reputation and trust with the public. CONCLUSION: Having identified the most important themes with suggestions for mitigating measures, Sanquin can take steps to become adaptive and proactive. Other blood establishments may also use a scenario approach to create contextualized long-term strategies.
Assuntos
Bancos de Sangue , Doadores de Sangue , Transfusão de Eritrócitos , Eritrócitos/citologia , Humanos , Países Baixos , Medicina de PrecisãoRESUMO
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). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on 'prepare, monitor, and adapt', enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.