Patients are not treated as one average profile
The underlying model can represent variation in age, disease stage, risk, test results, treatment eligibility, event timing, and survival.
Software / Underlying science
Individual patient modelling for rigorous health-economic evidence.
Medip applications are custom built for specific disease areas and pathways. Within each application, users adjust assumptions and run an individual patient model to produce transparent, documented analyses for publications, value dossiers, and market access evidence.
Individual patient simulation
Model patient heterogeneity, event histories, and long-term downstream outcomes.
HTA-oriented methods
Support analyses aligned with established assessment expectations such as NICE.
Publication-grade documentation
Trace application structure, model assumptions, inputs, analyses, and outputs for review.
Individual patient modelling
Patient-level pathways make complex value questions analysable.
Individual patient simulation is useful when outcomes depend on patient characteristics, pathway timing, event history, diagnostic accuracy, treatment sequencing, or long-term disease progression.
Events can happen over a lifetime horizon
After users select inputs in the application, the model can follow patients through diagnosis, treatment, monitoring, progression, complications, recurrence, death, and accumulated costs and QALYs.
Pathways can reflect real clinical logic
Diagnostic sequences, treatment choices, follow-up intensity, referral routes, false positives, false negatives, and downstream decisions can be represented explicitly.
Uncertainty remains testable
Scenario analysis, deterministic sensitivity analysis, probabilistic sensitivity analysis, and threshold analysis can show how robust conclusions are.
HTA standards
Aligned with established health technology assessment practice.
NICE and other HTA bodies emphasise clear decision problems, justified model structures, documented inputs, transparent assumptions, and sensitivity analyses. Medip applications make those elements visible before and after the model run.
Decision problem
Each application can define the population, intervention, comparator, outcomes, time horizon, perspective, and setting before results are interpreted.
Evidence and inputs
Clinical effects, utilities, resource use, costs, and assumptions are documented and linked to literature, guidelines, databases, or expert input.
Application and model structure
Application design, structural choices, health states, pathway events, extrapolations, and patient flow are available for review.
Reference-case and scenario outputs
Analyses can separate base-case results from alternative assumptions, scenario analyses, and non-reference-case perspectives.
From application to evidence asset
Structured for publication and market access evidence.
A documented application provides a reusable evidence asset for scientific communication, reimbursement planning, and local evidence adaptation.
Scientific publications
Transparent methods and reproducible assumptions
Documented application structure, model logic, parameter sources, uncertainty analysis, and outputs provide a basis for manuscripts, conference abstracts, and supplementary materials.
Value dossiers
A documented application for market access evidence
The same application provides a structured evidence base for global value dossiers, local value messages, reimbursement discussions, and evidence gap planning.
HTA submissions
Built for technical review
Inputs, assumptions, comparators, time horizons, perspectives, and sensitivity analyses are documented for technical review and country adaptation.
Stakeholder explanation
Complex science that can still be explained
Technical users can inspect model logic while non-specialists can see why results change across patients, pathways, and assumptions selected in the application.
Scientific transparency
Every conclusion remains traceable.
Users can see which inputs are evidence-based, which assumptions are local, which parameters drive uncertainty, and how changes propagate through costs, outcomes, QALYs, budget impact, and broader value.
Evidence sources
Link application parameters to trials, literature, guidelines, registries, cost databases, and expert elicitation.
Structural assumptions
Document why the application uses a specific pathway, health-state structure, time horizon, and patient flow.
Uncertainty analysis
Identify which parameters drive outcomes and when the innovation becomes favourable.
Reviewable outputs
Generate clear tables, figures, scenario outputs, and explanations for technical and non-technical review.
Join the discussion
Discuss the scientific logic behind your application
We can walk through the application structure, model assumptions, and HTA evidence requirements for your technology.
