Model Concept Developer

The Challenge

Building a health economic model begins long before any code is written or spreadsheet is opened. It starts with the hardest question in outcomes research: what is the right model structure for the decision problem at hand? Which health states should be included? How should disease progression be represented? What model type best fits the clinical context and the evidence landscape? Getting these structural decisions wrong is expensive. A model built on the wrong foundation requires costly rework, and the error often is not discovered until implementation is well underway or, worse, during regulatory review.

What makes this especially difficult is that the evidence needed to make these decisions does not arrive in one place. Understanding the disease landscape, the treatment options, the existing economic evaluations, and the clinical endpoints that matter requires surveying a broad range of published literature, clinical data, and regulatory context. A single health economist must absorb and synthesize information across multiple domains before they can even begin to articulate a coherent model structure, let alone document the rationale behind each design choice.

When this research and synthesis work is rushed or under-resourced, the consequences compound downstream. Structural assumptions go unexamined. Design choices are inherited from prior models out of convenience rather than fitness for purpose. Documentation is incomplete, creating reproducibility problems that surface during peer review or HTA submission. And when the researcher who conceived the model moves on, the rationale behind key decisions often goes with them.

How It's Done Today

In most research teams today, model conception begins with a literature search. A health economist spends days surveying the disease landscape: reading clinical guidelines, reviewing existing economic evaluations, cataloging treatment options, understanding how other modelers have approached the same therapeutic area. This is not a targeted extraction of specific parameters; it is a broad, exploratory effort to build enough context to make informed structural decisions. It is intellectually demanding work, and it is almost entirely manual.

Once the researcher has built a sufficient understanding of the evidence landscape, they begin synthesizing what they have learned into a model concept: which health states to include, what type of model fits the decision problem, how treatment effects should be represented, what cost and utility categories matter. This synthesis is where the real expertise lies, but it is also where the process is most fragile. The quality of the concept depends entirely on how thoroughly the evidence was surveyed, how carefully competing approaches were weighed, and whether the researcher had time to consider alternatives rather than defaulting to familiar structures.

The entire process, from initial evidence gathering to a documented concept ready for team review, typically spans two to three weeks for a moderately complex model. The resulting document is often incomplete: design rationale is missing, alternative approaches considered but rejected leave no trace, and the evidence base behind structural choices is inconsistently cited. For teams working under submission deadlines, corners get cut, and the model that eventually gets built may not fully reflect the decision problem it was meant to address.

The AI-Enabled Approach

You start by describing the decision problem you need to address: the disease area, the patient population, the country context, and optionally the model type you have in mind. You can also upload your own materials, such as an intervention document, published health economic models you want the concept to reflect, or relevant clinical papers. That is the entire setup. From there, Model Concept Developer takes over the evidence gathering and synthesis work that would otherwise consume your first two weeks.

The system launches parallel research across six evidence domains simultaneously: disease overview, disease burden, treatment landscape, clinical endpoints, existing economic evaluations, and regulatory context. It surveys published sources, extracts relevant findings, and synthesizes them into a structured evidence base. This is the same contextual foundation a senior health economist would build manually, but assembled in minutes rather than days. If you uploaded your own materials, the system reconciles what it found against your documents, flagging conflicts and incorporating insights specific to your treatment context.

From that synthesized evidence base, the system develops a structured model concept document across eight categories: patient population, clinical events and health states, interventions, treatment efficacy, treatment safety, costs, utilities, and model structure. Every design choice is grounded in the evidence gathered and cited to its source. The system does not assume a model structure in advance: it evaluates your decision problem and disease area and recommends the methodology, whether that is a Markov cohort model, a partitioned survival approach, or another framework, that best fits your specific situation.

What you receive is a complete, citation-ready concept document: health states defined, structural assumptions stated and justified, key parameters identified, and the evidence rationale behind each design decision recorded in traceable form. From there, you can ask questions, request revisions, or explore alternative approaches through a conversational interface backed by the full evidence base the system assembled. You can also edit the concept document directly for small changes you prefer to make by hand. The initial concept is ready in minutes; the refined, team-ready document typically takes hours, not the weeks the process has always demanded.

What It Means for You

• The evidence gathering and synthesis work that normally consumes the first two to three weeks of model development is completed in hours, freeing your team to focus on the design decisions that require genuine expertise.
• You receive a methodology recommendation grounded in the evidence landscape of your specific decision problem and disease area, not a default structure chosen out of habit or convenience.
• Every structural choice in the concept document is cited to its evidence source, giving your team and stakeholders a transparent record of why the model is designed the way it is.
• If you provide your own intervention documents or published model examples, the system reconciles them against its research findings, ensuring the concept reflects your specific treatment context rather than generic disease-area assumptions.
• Non-technical stakeholders can read and validate the concept document without requiring a modeler to translate it for them, accelerating alignment across the project team.
• Questions and revisions are handled through a conversational interface backed by the full evidence base, so refining the concept is a matter of minutes, not another round of literature review.