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June 16, 2025

Editor’s note: This is the third of four articles in our series on the crucial role health economic modeling plays in evaluating the clinical and economic impacts of healthcare interventions. The series will focus on common modeling approaches, including cost-effectiveness analysis, and highlight how these techniques have become indispensable tools for HTA submissions and global reimbursement decisions.

Understanding the financial implications of new healthcare interventions is fundamental to making informed reimbursement and access decisions. While cost-effectiveness analysis (CEA) has long been regarded as a critical tool in evaluating the value of new treatments, budget-impact analysis (BIA) offers a complementary perspective by focusing on short- to medium-term affordability and sustainability. Together, they create a comprehensive framework that empowers payers in balancing long-term value with near-term financial feasibility.

The third article in our health economic modeling series explores how BIA complements CEA and key steps to ensure accuracy and reliability when conducting an analysis.

Read the second blog in our series ‘Using cost-effectiveness models to drive smarter drug development and access decisions’.

What is a budget-impact analysis (BIA)?

A budget-impact analysis evaluates how the adoption of a new medical technology impacts a payer’s budget over a defined timeframe. Unlike a cost-effectiveness analysis, which typically involves a head-to-head comparison of two or more specific treatment options to assess long-term value (e.g., using quality-adjusted life years or QALYs), a BIA compares two broader scenarios: the healthcare system’s financial status quo versus the projected budgetary outcomes after introducing the new technology. A BIA estimates the size of the eligible patient population, forecasts how rapidly the technology will be adopted (uptake rates), and calculates associated costs (i.e., drug acquisition, administration, monitoring, and hospitalization) under realistic market conditions, over a defined short- to medium-term horizon, typically 1 to 5 years. It explicitly considers all relevant current and future treatments, including their market shares and dynamics, rather than focusing solely on one comparator. This allows BIAs to reflect realistic clinical practice, in contrast to CEAs which may oversimplify comparator structure. Key modeling inputs include epidemiological data (such as disease prevalence, incidence, and mortality rates), current and anticipated standards of care, market trends, and healthcare utilization patterns. Costs are analyzed from the payer’s perspective, with detailed consideration given to patient access pathways, intervention mix, and changes to resource allocation resulting from the new technology’s introduction.

While BIA is generally more straightforward than CEA, many HTA guidelines (i.e., ISPOR, NICE) recommend incorporating sensitivity or scenario analyses to explore uncertainty in key inputs and assumptions, even if not to the same extent as in CEAs.

Why budget-impact analysis matters:

  • Affordability: Helps payers determine whether integrating a new technology fits within immediate budget constraints for a defined budgeting period (typically 1-5 years).
  • Sustainability: Evaluates short-to-medium-term financial feasibility to ensure that ongoing patient access can be maintained without compromising financial stability.
  • Practical Decision-Making: Provides detailed insights into expected patient uptake and treatment utilization patterns, enabling more accurate forecasting and better allocation of healthcare resources.

How does a budget-impact analysis (BIA) complement a cost-effectiveness analysis (CEA)?

Cost-effectiveness analysis (CEA) highlights long-term value while budget-impact analysis (BIA) focuses on near-term financial feasibility. Together, these approaches present a comprehensive picture to decision makers.

1. Focus on Different Metrics

CEA evaluates the long-term value of a healthcare intervention and broader societal benefits, whereas BIA zeroes in on financial feasibility and short- to medium-term budgetary impact.

2. Actionable Insights for Payers

While CEA indicates whether a medical technology offers value, BIA determines if the technology is financially sustainable within the healthcare system.

3. Alignment with Policy Decisions

CEA informs decisions related to long-term health benefits and system efficiency, while BIA provides essential data for near-term considerations such as pricing negotiations, reimbursement policies and formulary inclusion.

For instance, a model-based study explored the clinical and economic impact of integrating a radiolabeled form of estrogen that binds to both alpha and beta ER ([18F]FES), for use with PET imaging/CT into biopsy/immunohistochemistry (IHC) to determine estrogen receptor (ER)-positive status in metastatic and recurrent breast cancer. The cost-effectiveness analysis demonstrated that implementing [18F]FES PET/CT consistently improved diagnostic accuracy compared to the status quo, increasing the proportion of true-positive and true-negative results across all patient groups. This enhanced precision led to better treatment decisions, reduced disease progression, fewer repeat biopsies, and improved patient outcomes (QALYs).

In parallel, a budget-impact analysis showed that these clinical improvements translated into cost savings over a five-year horizon, primarily due to reduced unnecessary interventions and lower re-biopsy rates. This dual perspective highlighted both the long-term value and near-term affordability of [18F]FES PET/CT, strengthening the case for reimbursement and uptake.

Since cost-effectiveness analysis (CEA) typically relies on lifetime or long-term horizons, its conclusions may be less immediately relevant in geographies where payers operate under annual or biennial budget constraints. In such settings, budget impact analysis (BIA) is often the more appropriate standalone financial assessment, reflecting local decision-making needs and requirements.

Conducting a robust BIA requires a systematic approach to ensure accuracy and reliability. Here are the essential steps:

Step 1: Population Assessment

  • Identify the target population and relevant subpopulations using epidemiological and clinical data.
  • Determine prevalence, incidence, and other factors to assess the size of the eligible patient pool.
  • Forecast patient numbers based on different uptake scenarios (e.g., gradual vs. immediate adoption).

Step 2: Scenario Analysis

  • Model multiple scenarios, comparing the current standard of care (status quo) with the introduction of the new technology.
  • Predict market share shifts and the adoption rate over a defined timeframe (e.g., five years).
    Incorporate real-world data, including potential discontinuation due to insufficient efficacy, switching to default treatments, and side-effect management, to ensure accurate projections.

Step 3: Cost Allocation

  • Assign costs to treatments, including drug acquisition, diagnostics, monitoring, adverse event management, and follow-up care.
  • Factor in resource utilization, such as hospitalizations, specialist consultations, and other healthcare services.
  • Estimate budget impact based on incremental costs of introducing a new medical technology versus maintaining the status quo.

These steps provide payers with the clarity needed to define patient access strategies, set co-payment levels, and identify priority subpopulations. In addition, BIA supports planning appropriately for scenarios involving treatment discontinuation or adverse event management.

Integrating insights from BIA and CEA into submissions

Budget-impact analysis is no longer optional; it’s a critical component of market access strategy. With healthcare budgets under constant strain, payers require clear, actionable insights to make informed decisions. By focusing on affordability and sustainability, BIA ensures that new technologies can be integrated without compromising access or quality. Market access, pricing, and reimbursement leaders must prioritize integrating BIA into their decision-making processes. Combined with cost-effectiveness analysis, it positions organizations for long-term success by balancing value with financial feasibility.

Health Technology Assessment (HTA) submissions and Global Value Dossiers (GVD) tell a holistic story that must include clinical and financial evidence. A BIA plays an important role in demonstrating the value of new treatments. We’ll wrap up our blog series with a look at the full spectrum of information presented in HTA submissions and GVDs to build a case for new medical technologies.

Health Economics and Advanced Modeling capabilities

To learn more about Certara Evidence & Access and our budget-impact modeling services, visit our Health Economics and Outcomes Research (HEOR) page.

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Kinga Pacocha

Associate Director, Evidence & Access

Kinga Pacocha has 15+ years’ experience in Health Economics & Outcomes Research and Market Access. She specializes in budget impact modeling, cost-effectiveness modeling, and RWE cost-consequences analyses in many therapeutic areas (e.g. hematology, allergology, urology, rare diseases, oncology, diabetology, obstetrics and gynecology). Kinga has contributed to 50+ Health Technology Assessment projects focused on the Central and Eastern European (CEE) market, numerous managerial calculators used for the strategic and negotiation purposes in reimbursement processes as well as global health economic models from scratch. She’s also coauthored questionnaire studies with KOLs and contributed to over a dozen HEOR publications.

Noemi Hummel

Associate Director

Noemi holds a PhD in economics from the Swiss Federal Institute of Technology Zurich, and a master’s degree in applied mathematics from University of Heidelberg. She has more than 10 years of experience in working as a senior biostatistician and modelling projects direction. In Certara she has been involved in projects on statistical and economic modelling and applying evidence synthesis methods such as Bayesian network meta-analysis and model-based meta-analysis in a variety of disease areas including neuroscience, oncology, and rare diseases.

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