In risk management, whether or not risk analyses and probability calculations can be carried out using stochastic instruments depends heavily on the area, the topic and the risk details. If we look at high-risk organisations, which includes a hospital, risks, so-called low-probability high-consequence risks, play an increasingly important role. Low-probability high-consequence risks are often also referred to as tail risks. The term "tail risk" refers to events or situations that lie at the edge of the probability distribution, i.e. occur statistically rarely. These risks are often located in the "tails" of the probability distribution curve, hence the term "tail risks".
This type of risk therefore makes it de facto impossible or, to put it another way, impractical to work with statistical models such as Monte Carlo simulations. This technique is particularly useful in complex situations with many uncertain variables, as it is a proven method for assessing the risks or variables. Common applications of Monte Carlo simulation usually work with several thousand to ten thousand samples to achieve meaningful results.
Yes, it is possible to work stochastically with a small amount of data, but the results may be less accurate and subject to greater uncertainty. Stochastic models are based on probabilities and statistical assumptions and therefore usually require a sufficient amount of data to enable accurate predictions and robust conclusions.
Professional or expert judgements refer to assessments and decisions made by professionals based on their experience, expertise and judgement. The advantage is that experts can often provide insight and background into complex issues that cannot be captured by purely statistical or mathematical models. Their judgements can be particularly valuable when data is limited or unforeseeable events are involved, as they are based on personal experience and specialist knowledge. Expert judgement can therefore help to better assess risks and make informed risk management decisions. There are incredibly interesting research results and studies on the subject of forecasting ability in specialised teams. The topic of "superforecasters" ("The Art and Science of Prediction" by Philip E. Tetlock and Dan Gardner) is a science in its own right, but is closely related.
But this applies not only to low probability and high consequence risks, but also in many aspects to the strategic component. This is because there is naturally little or no meaningful retrospective data available for most strategic issues.
The risk manager's responsibility includes the well-founded selection and definition of the appropriate risk management strategy for various risk areas, topics and time horizons. It is crucial to determine the optimal approach - be it stochastic based on quantitative models, qualitative based on expert judgement or a combination of both approaches. The risk manager must be able to justify and define the respective instruments and methods in order to ensure an effective and comprehensive risk assessment. By carefully analysing and weighing up the different approaches, a systematic and efficient risk management strategy can be developed that meets the company's requirements.