Embracing Uncertainty in Healthcare

Why did we organise this event?

Do you always set plans and rarely achieve them because by the time they are published things changed? Do you generate a forecast and it’s always wrong? It’s not your fault if your forecast is wrong and your plan fails – traditional deterministic forecasting and planning is seriously incomplete because it ignores uncertainties.

At present, healthcare planning and forecasting is primarily deterministic. Decision making based on deterministic analysis are made based on exact numbers (average) and generally follows a “go-no go” framework. If we forecast and plan too low, we cause shortages and plan disruption, if we forecast and plan too high, we create waste and inefficiency. Therefore, decision making based on deterministic analysis may result in aggressive or conservative cases which may lead to suboptimal plans. Moreover, changes in patients’ needs, demography, regulatory framework, health services triggered and driven by crisis, innovation in treatments, technologies and care models introduce new uncertainties that cannot be solely addresses by deterministic forecasting and planning.

Healthcare planers are looking for approaches to address challenges related to uncertainties in the sector. Probabilistic forecasting and planning techniques have the potential to address these concerns and consider various type of uncertainties facing planners in more rigorous manner. There is a clear need in the healthcare to highlight how probabilistic forecasting and planning can be used to address uncertainty challenges facing the sector.