Budget impact models for lung cancer interventions: A systematic literature review
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View abstract on PubMed
Summary
This summary is machine-generated.Budget impact models (BIMs) for lung cancer show variability and room for improvement in forecasting financial implications. Adherence to best practices was inconsistent, highlighting a need for enhanced model quality in healthcare decision-making.
Area Of Science
- Health economics
- Pharmacoeconomics
- Oncology
Background
- Budget impact models (BIMs) are crucial for assessing new healthcare technology adoption and budget reallocation.
- Discrepancies exist between BIM estimates and actual financial outcomes.
- An AI-driven tool is being developed to identify non-small cell lung cancer patients benefiting from immunotherapy.
Purpose Of The Study
- To evaluate the budgetary implications of lung cancer interventions.
- To conduct a systematic literature review of published BIMs for lung cancer.
Main Methods
- Systematic literature search of PubMed and EMBASE (2010-2023) for lung cancer BIMs.
- Extraction of model characteristics, including interventions, costs, and adherence to best practices.
- Evaluation against Professional Society for Health Economics and Pharmacoeconomics Research guidelines.
Main Results
- 25 BIMs were identified across 14 countries, with variable model structure clarity.
- Cost calculator approach was common; time horizons were generally appropriate.
- Market uptake assumptions were poorly documented, and cancer-related costs were rarely included.
- Adherence to best practices was inconsistent and did not improve over time.
Conclusions
- The number of lung cancer BIMs published is substantial, with modest improvements in frequency and quality.
- Significant variability and suboptimal adherence to best practices were observed.
- While no single model was ideal for evaluating AI tools, insights were gained for future development.
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