NHS planning has, as a result of the 10-Year Health Plan for England, had to evolve rapidly. The new medium-term framework expects healthcare providers to manage three-year plans, including revenue forecasting. This means forecasts must be reliable for longer than ever before.

Fortunately, more accurate, sustainable forecasting is achievable with reliable data and continuous adjustment. With driver-based models and rolling forecasts, finance teams have a genuine opportunity to help providers manage this transition with more confidence.

Stop forecasting from the rear-view mirror

NHS finance teams already manage some of the most complex financial forecasting in the public sector. The difference now, however, is there is a longer planning horizon ahead.

While historical data provides a useful foundation for observing financial performance, it can't be relied on to predict the long-term future. Factors such as resource demand, funding, service activity and workforce costs constantly shift the broader financial picture.

For example, the medium-term planning framework aims to eliminate agency spending by 2029/30. Historical staffing expenditure, therefore, is a less reliable guide when followed on its own.

Moreover, NHS finance teams that forecast purely on historical financial data risk reacting late to change and having limited options to pivot.

To keep pace with potential changes, healthcare finance teams should move toward forecasting from the factors that actually drive costs and income. Analysts can keep those forecasts current with AI-powered finance tools that consolidate activity, workforce and income data continuously, leaving more time to interpret what the numbers mean.

The knock-on effect is finance teams can now spot change earlier, meaning they have more room to plan.

Move beyond static budgets with driver-based forecasting

Focusing on core cost and income drivers is a key way to move away from static, fixed assumptions.

Healthcare finance analysts should consider drivers including patient volumes and activity, staffing levels, payment scheme income, service demand and bed and ward occupancy.

These drivers shift across the seasons. For example, bed occupancy climbs as winter illnesses such as flu and Covid drive up admissions. Occupancy climbed higher than planned in January 2026, which saw 95.7% occupancy in England against a 92% operational planning limit.

This means more bank shifts, pressure on agency caps and an overall higher pay bill. It is a cost gap that a static budget would not have anticipated.

It is also where driver-based forecasting offers much-needed support. When occupancy rises, forecast staffing costs rise alongside it. It also helps finance and operations to work from the same numbers and finance managers see why forecasts move.

Build forecasts that can adapt to what comes next

Conditions rarely, if ever, hold steady across a financial year, meaning fixed forecasts risk falling behind. Moving toward a more adaptable, reliable system means linking together three key cycle elements: scenario modelling, rolling forecasting and regular variance analysis.

Advanced scenario modelling, supported by AI, helps finance teams plan for expected, better and worse cases. The NHS already uses AI to predict A&E demand, and the same principle helps to sharpen financial forecasts. AI and automation make it quicker and easier for teams to build and compare more scenarios, including less likely ones.

Using January's 95.7% occupancy as a baseline, finance teams are able to model and plan for what happens if conditions change and demand rises even further.

Rolling forecasting moves teams away from static, fixed predictions toward more regular updates. These depict current financial health while looking ahead across a specific timeframe (eg, the next 12 to 18 months). Crucially, this suits current NHS planning expectations, as each update keeps extending the view further ahead.

Regularly analysing variance means comparing actual figures with forecasts, finding the cause of change and adjusting drivers as a result. For example, if bank spend runs higher than forecast by the third month, finance can trace it back to occupancy changes.

Using automation here not only supports more accurate insights but also reduces manual spreadsheet work.

Scenarios prepare finance teams, rolling updates keep forecasts current and variance analysis sharpens the next forecast. Combining these elements helps healthcare finance teams to develop more reliable, resilient forecasts, which better support the plans NHS England now expects.

Turning better forecasts into better decisions

More accurate, adaptable and shared forecasts give healthcare leaders clearer insight into a provider's financial health and flexibility. These forecasts also support more confident decision-making, especially amid changing pressures and when deciding where staff and funding should go.

Whether that means opening escalation beds or funding alternative staffing routes, the best decisions are made earlier, backed by evidence.

With another winter fast approaching, now is the time for finance teams to move away from static forecasts and consider how technology helps them meet patient and Government demands.