Capacity
Capacity Planning & Assurance
Standard capacity planning models averages, but NHS wards and bed pools run on stochastic demand where arrival rates vary in ways averages cannot capture. The deeper problem is structural: bed allocation rules, escalation protocols, and flow policies, individually reasonable and collectively governing, can interact to make performance targets mathematically infeasible before resource levels are even considered.
What this delivers
A logic twin of the acute footprint
A validated computational model of your bed pool, ward policies, and patient flow interactions, fitted to real demand and flow data from your trust data warehouse. The logic twin is validated against historical performance before any analysis begins.
Structural infeasibility verdict
A formal answer to whether your bed and flow targets are achievable under the current pathway design. If the constraint is structural rather than capacity-related, the assessment identifies the nature of the problem and the basis for resolution, preventing resource commitment to a recovery programme that cannot succeed.
Optimal configuration for real variance
The bed count, allocation policy, and escalation thresholds that deliver the target under actual demand distributions, not averages. Where capacity is the binding constraint, the assessment states exactly how much and at which nodes.
Winter surge profile with reserve thresholds
A quantified surge model derived from historical demand patterns, with the exact reserve capacity required to maintain target performance during peak periods. Suitable for winter planning submissions and NHS England assurance.
Elective RTT modelling
18-week RTT performance modelled within the current funding envelope, with the specific configuration changes required to achieve the target. Board-ready report, interactive model output, and briefing delivered within six weeks of data receipt.
How it works
We begin with a Baseline: extracting activity data, flow records, and demand patterns from the trust data warehouse, and fitting statistical distributions to arrival and service processes. We observe the ward or acute footprint directly to map the informal rules and escalation conventions that govern bed management in practice, including decisions that data alone does not capture.
From this baseline we build the logic twin: a validated computational model of the pathway's policies, bed allocation rules, and flow interactions. The logic twin makes every governing constraint explicit and testable. We validate it against two years of historical performance data before any scenario work begins. Where the model does not reproduce observed performance, we investigate further before proceeding.
Against the validated logic twin we model the full range of configurations (bed numbers, allocation policies, escalation thresholds) and identify the optimal configuration for real demand variance. We produce the winter surge reserve profile and elective RTT model within the same engagement. The output is a verdict and a decision-ready analysis. Where the constraint is structural rather than capacity-related, we identify the minimum change required to resolve it before recommending any resource commitment.