Module 1: Foundations of Budget Impact Analysis

This module explains the rationale for budget impact analysis (BIA), the basic architecture of a budget impact model, the types of healthcare decision-makers that use it and the standards models must comply with.

• the distinction between value and affordability – and why payers must evaluate both
• anatomy of a BIA – comparing alternate healthcare realities
• stakeholders and their perspectives – who uses BIAs and how do they use them?
• the ISPOR Principles of Good Practice as the foundation of this course
• the wonderful world of BIAs – comparing practices across settings and sectors

Module 2: Conceptualising a Budget Impact Model

This module introduces the four essential components that comprise the analytic framework of a BIA: the eligible population, time horizon, treatment pathway and cost perspective.

• the analytic framework that enables you to plan a successful analysis
• the eligible population for a treatment or intervention
• choosing an appropriate time horizon
• the current treatment or intervention mix and how it is expected to change
• deciding on a cost perspective

Module 3: Populating the Model 1 – Estimating the Eligible Population

This is the first of three modules exploring, in detail, how to build and populate a budget impact model. In this module we learn how to tackle the first critical step in developing a BIA: estimating the size of the population that is eligible for a new treatment or intervention.

• defining and distinguishing between the prevalent and incident populations
• funnelling down from total population to eligible patients.
• sourcing demographic and epidemiological data
• estimating population size by patient subgroup
• modelling populations that change over the time horizon of a model

Module 4: Populating the Model 2 — Estimating the Treatment Mix and Market Uptake

Once the population that is eligible for a new drug or device has been estimated the next step is to forecast how this new technology will, over time, influence the mixture of treatments patients receive. However, characterising the world with and the world without a new treatment is far from straightforward.

Module 5: Populating the Model 3 — Estimating Cost and Resource Use

This module identifies, sources and structures the cost inputs that convert the model’s population and treatment mix into financial consequences.

• identifying cost categories — intervention costs (acquisition, administration, monitoring) and condition-related costs (adverse events, complications, downstream resource use) from the payer’s perspective
• prices versus costs — list prices, confidential discounts, tariffs and reimbursement mechanisms, and why the price the model needs is the price the budget holder actually pays
• sourcing unit costs — NHS Reference Costs, PSSRU, BNF, eMIT and supply chain catalogues, and their international equivalents
• gross versus net budget impact — calculating cost offsets and understanding when each measure is appropriate for which audience
• budget structure complications — one-off versus recurring costs, capital versus revenue expenditure and the problem of NHS budget silos, where savings accrue to a different budget than the one bearing the cost

Module 6: Building the Budget Impact Model in Excel

This is the core practical module in which participants build a fully functional budget impact model step by step, following a structured workbook architecture.

• workbook architecture — designing the model structure: inputs, population, market share, costs, calculation engine, results and scenario sheets
• good modelling practice — transparent formulae, colour-coding conventions, named ranges, error checks and version control, drawing on ISPOR-SMDM guidelines
• building the calculation engine — connecting population, market share and cost inputs into the scenario calculations that generate the ‘world with’ and ‘world without’ totals
• producing the results — annual and cumulative budget impact, gross and net results, and disaggregated outputs that show decision-makers where the money moves
• verification and validation — error-checking, face validity testing, extreme value analysis and replication, ensuring the model is credible and defensible

Module 7: Uncertainty, Sensitivity and Scenario Analysis

This module teaches participants how to identify, quantify and analyse the uncertainty inherent in every budget impact analysis.

• sources of uncertainty in BIA — parameter uncertainty, structural uncertainty and methodological choices, and why uptake and population estimates dominate in practice
• deterministic sensitivity analysis — one-way and multi-way analysis to identify the parameters that most influence the results
• visualising the drivers — tornado diagrams and threshold analysis: finding the values at which the budget impact conclusion changes
• scenario analysis — designing best case, worst case and base case scenarios that reflect genuine decision uncertainty rather than arbitrary variation
• probabilistic approaches — when probabilistic sensitivity analysis adds value in BIA, why it is used less than in cost-effectiveness modelling and how to implement it when required

Module 8: Interpreting, Presenting and Defending Budget Impact Results

This final module equips participants to report, communicate and defend their findings with the audiences that matter — HTA reviewers, payers and budget holders.

• reporting the results — presenting BIA findings in HTA submission dossiers, NHS business cases and payer value propositions, following ISPOR reporting standards including disaggregated results
• communicating with budget holders — building an affordability narrative that connects financial projections to the payer’s strategic priorities, including the honest presentation of uncertainty
• anticipating critique — how HTA reviewers assess BIA submissions and the pitfalls that undermine credibility: double-counting, inappropriate horizons, underestimated populations and optimistic uptake
• from model to tool — converting a static analysis into an interactive budget impact model that payers and field teams can adapt to their local populations and prices
• defending and updating the analysis — responding to reviewer challenge, revising the model as new evidence emerges and validating projections against real-world outturn