Patient flow

Patient flow analysis identifies, with data, where delay is really concentrated, which does not always coincide with where it is perceived. It applies when a department is overcrowded, whether that means a gridlocked emergency department, surgical waiting lists or a shortage of beds, and it is not clear whether the bottleneck lies in capacity or in how patients move through the system. Adding resources at a point that is not the limiting one may not relieve the overcrowding and can use up budget to no effect. The analysis reconstructs the patient's path from activity records, locates the points where delay builds up and characterises how demand varies. The result is a quantitative map of where delay is concentrated and which point limits the flow, so that you can prioritise the intervention instead of allocating resources blindly.

You need activity records with reliable timestamps: admissions, discharges, waiting times, surgical activity and procedure durations, depending on the flow you want to analyse. The hospital provides these data; the service links them, checks their temporal consistency and reconstructs the complete patient journey from the available sources. As well as timestamps, you will usually need episode, patient or resource identifiers that allow the stages of the path to be linked without breaking traceability. The quality of the analysis depends directly on how reliable the timestamps are: if the dates and times of each step in the process are not consistent, the map of the flow is distorted. That is why reconstructing the path and checking temporal consistency are a step before the analysis, not a formality. The data do not need to arrive unified, since linking different sources is part of the work, but they do need to record the key moments of the patient's passage through the system.

The bottleneck is identified by analysing where delay builds up and which stage limits the capacity of the whole flow, not where overcrowding is most visible. This applies to any flow where the wait perceived at one point may originate at an earlier one. The analysis characterises the variability of stays and of the times at each stage of the process, and locates the points that limit the capacity of the whole. When the flow justifies it, queueing theory is used: the framework that models how waits form from the relationship between patient arrivals, service capacity and their variability. This makes it possible to distinguish structural delay, where the available capacity repeatedly proves insufficient under certain demand patterns, from one-off delay caused by a peak in demand. Identifying the real limiting factor avoids investing in expanding a stage that is not the one holding the system back.

Yes. The service can estimate quantitatively the likely effect of a change before resources are committed to it, through scenario analysis. This applies when you are considering reorganising surgical blocks, changing the admission policy or resizing a department, and want to compare options without trying them out on the real system. The "what-if" analysis starts from demand modelling and, when the problem calls for it, draws on discrete-event simulation, which reproduces the behaviour of the flow step by step to anticipate how times and occupancy would respond to each change. The comparison of scenarios should make explicit the assumptions about demand, capacity, service times and operating rules that were used. It is worth being clear about the scope: the result is an estimate of the likely effect under the model's assumptions, not a promise of what will happen. Its value lies in allowing scenarios to be compared on a quantitative basis, instead of deciding by intuition. The decision and its implementation belong to the centre.

Discrete-event simulation is a technique that reproduces how a system works by simulating, one by one, the events that make it up, such as arrivals, admissions, operations or discharges, to observe how the flow behaves as a whole. It is used when the problem is complex or interdependent enough that a static analysis is not sufficient: when there are shared resources, high variability or knock-on effects that an aggregate calculation does not capture. It lets you ask questions such as "what could happen to waiting times if an operating theatre is added, the schedule changes or a priority rule is modified?", recreating the flow virtually before touching the real system. Its quality depends on the distributions of arrivals, durations and system rules being well estimated or, at least, clearly stated as assumptions. Not every project needs it: many are resolved with statistical analysis of demand and capacity, and simulation is reserved for cases whose complexity justifies it. It is one of the service's tools, not its only method.

No. The service provides the analysis and the quantitative evidence; redesigning the process and implementing the changes are the hospital's responsibility. The distinction is deliberate. Quantifying where the delay lies, estimating capacity needs under given scenarios and estimating the likely effect of a change is analysis. Reorganising circuits, allocating staff and carrying out the operational transformation is healthcare management, which belongs to the centre's own team, who know its constraints and context. What the service delivers is the quantitative basis for making the decision and defending it to senior management: which point limits the system, what capacity might be needed under each demand and organisational assumption, and what effect can be expected from each scenario. This separation protects the client, because a reorganisation defined without the operational knowledge of the department may not be workable. The service measures and anticipates; the centre decides, redesigns and implements.

Patient flow optimisation focuses on patient movement and capacity: where delay builds up, how demand varies, and how to size beds, operating theatres and clinics. Hospital cost and operational efficiency analysis focuses on the consumption of financial resources: how much each process costs, where the cost variability lies and which units have room to become more efficient. They do not answer the same question: one focuses on patient movement, times and capacity; the other, on the economic consumption of resources and operational efficiency. They complement each other, since easing a bottleneck usually has an economic effect and a cost analysis can point to where to look at the flow, but they start from different data (activity and times versus costs) and inform different decisions: one about capacity and organisation, the other about the economic allocation of resources. Patient flow analysis can later feed an economic analysis, but it does not replace it.

When simulation is applied to patient flow, waiting times and hospital capacity, it falls within patient flow analysis: it is one of its techniques, not a separate service. This answers the natural question of anyone who sees "simulation" in two places in the catalogue. The difference lies in the object, not the method. Here, discrete-event simulation models the behaviour of a specific care flow, whether in the emergency department, the operating theatres or the beds, to analyse capacity and times. The cross-sector scenario simulation and policy evaluation service applies simulation to other contexts, such as the impact of a policy or the evolution of a population, where the question is not hospital flow but the effect of a decision at another scale. If the problem is one of capacity and patient movement, this is the service; if the question is the evaluation of a policy, a population intervention or a scenario outside the specific hospital setting, the cross-sector scenario simulation service is a better fit.