BACKGROUND:
Breast cancer remains the most common malignancy and leading cause of cancer-related death among women worldwide. While localised disease is largely curable, metastatic or recurrent cases carry a poor prognosis and present significant challenges for healthcare systems. The NHS faces unprecedented pressures due to resource scarcity, capacity constraints, and the rapid introduction of innovative therapies, particularly in advanced breast cancer (ABC). Existing budget impact (BI) assessments, such as those provided by the Scottish Medicines Consortium (SMC), often rely on assumptions and expert opinion, failing to capture the complexity of real-world decision-making. This can result in either over- or underestimation of budgetary needs, leading to opportunity costs or unexpected debt. There is a pressing need for actionable real-world evidence (RWE) and more rigorous, data-driven methodologies to inform budget impact analysis (BIA) and support sustainable, value-based healthcare.
AIMS & OBJECTIVES:
The overarching aim of this research was to advance health data intelligence capability within NHS Lothian by developing a model-based, real-world data (RWD)-driven approach to BIA. This approach seeks to provide more accurate predictions of the cost implications and resource demands associated with adopting new therapies for advanced breast cancer, thereby informing regional guidance and supporting feasible, affordable care provision. Specifically, this work leverages Scotland’s rich RWD assets and patient-level simulation modelling to deliver a structured, accurate BI forecasting tool for new drugs in Secondary Care. The objective was to develop a proof-of-concept budget impact model (BIM) using exemplar new drugs for ABC within the Edinburgh Cancer Centre, providing a robust, data-driven solution to NHS budget pressures.
RESEARCH QUESTIONS:
1. What are the current methods for BIA in healthcare?)
2. How can we improve current BIA by better use of RWD and the application of new or improved methods?
3. Can new approaches improve the adoption process for an exemplar new treatment of advanced breast cancer in Scotland?
METHODS:
The study targeted female post-menopausal patients diagnosed with ER+ / Her2- metastatic or locally advanced breast cancer between 2013 and 2017 within NHS Lothian, who were previously untreated for their advanced disease. Regional and national routinely collected data were linked to reconstruct and segment clinical pathways for CDK4/6 inhibitor-eligible ABC patients (N=74). Analyses were conducted within the National Safe Haven/ DDI DataLoch trusted research environment (TRE), extracting non-disclosive summary statistics and parametric model distributions. The BIM prototype, constructed and implemented in R/Studio, uses a probabilistic patient-level state-transition model as its structural core, integrating both published parameters and those derived from descriptive analysis of real-world patient-level data. The model captures incidence, prevalence, and cost dynamics over a 5-year time horizon, allows scenario analyses, and generates QALYs alongside financial estimates—thereby interfacing cost-effectiveness and BI modelling.
FINDINGS:
A user-friendly prototype BIM was developed, revealing substantial discrepancies between RWD-based and SMC BI template estimates. The SMC calculator significantly underestimated the 5-year BI (£5,851,911 for 1,700+ patients), compared to the BIM’s estimate (£4,045,092.27 for 121 simulated patients). Accurate BI estimation is crucial: overestimation risks opportunity costs, while underestimation can lead to overspending and debt. The new BIM successfully integrated QALYs and offers a flexible, scenario-analysis-based framework for more realistic BI forecasting for CDK4/6i-eligible patients. Findings also highlighted interoperability and analytical challenges, necessitating further validity checks and testing the generalisability of the BIM prototype.
CONCLUSIONS:
BIA provides policymakers and NHS managers with essential estimates of the financial and service implications of adopting new healthcare technologies, complementing cost-effectiveness analysis by offering a temporal and quantitative perspective on resource and cost changes. This pioneering study demonstrates how real-world data can be converted into actionable, evidence-backed insights that enhance existing economic evaluations. The proposed BIM addresses persistent unmet needs of Scottish health technology assessment by bridging the gap between value and affordability, supporting the NHS objective of delivering value-based healthcare while maintaining fiscal sustainability
