Iterative Decision-Making Framework for Model-Based Decision Making
A structured approach that helps decision-makers build and use models in a step-by-step, repeating process to support health care and resource allocation decisions.
At a glance
Use when
Developing or using decision models in dynamic environments with evolving evidence or stakeholder needs
Avoid when
A one-time, static decision analysis is sufficient and no further updates are expected
Inputs
Clinical evidence, economic data, stakeholder input, decision context
Outputs
Model-informed recommendations, updated models, decision documentation
How it works
This framework provides a coherent sequence of iterative steps for developing and applying decision-analytic models in health technology assessment, ensuring alignment between model development and evolving decision needs, often used in Medical Decision Making and supported by institutions like the Centre for Health Economics at York.
- HTA domains
- Clinical Effectiveness, Costs & Economic Evaluation, Organisational aspects
- Assumptions
- Decision needs evolve over time; models should adapt accordingly; stakeholder engagement improves decision quality
- Strengths
- Promotes transparency, adaptability, and stakeholder alignment; supports learning over time
- Limitations
- Requires ongoing resources and commitment; may delay decisions if iterations are too frequent
Questions this answers
- › How should decision-analytic models be developed to best support real-world health decisions?
- › What steps ensure models remain relevant as new evidence emerges?
- › How can modeling be integrated into ongoing decision-making processes?
- › When should a model be updated or refined?
- › How can stakeholders be engaged throughout the modeling process?
- › What makes model-based decisions more transparent and trustworthy?
References & sources
Similar by meaning
- Conceptualizing a Model (ISPOR-SMDM Modeling Good Research Practices Task Force-2)
- DARTH Decision-Analytic Modeling Coding Framework (A Need for Change!)
- Framework for Addressing Structural Uncertainty in Decision Models (Bojke et al.)
- A Framework for Developing the Structure of Public Health Economic Models
- Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial
Beta record. Based on the original catalogue summary; primary-source enrichment pending.