Model Specifications Developer

The Challenge

Once the structural design of a health economic model has been decided, the next step is to fill in the detail: the specific parameter values, transition probabilities, efficacy estimates, cost inputs, and utility weights that will drive the model's calculations. This is the model specification: the implementation-ready blueprint that tells a developer exactly what to build. Every element in it must be grounded in published evidence and documented with enough rigor to satisfy HTA reviewers, payers, and regulatory bodies.

The challenge is that the evidence needed to build a specification is spread across multiple published articles, each with its own methodological choices, parameter definitions, supplementary tables, and assumptions. Different authors may define health states in subtly incompatible ways, report efficacy using different measures, or source costs from different perspectives. Synthesizing those sources into a single coherent specification, and doing so systematically across five core modeling domains, requires reading and cross-referencing hundreds of pages of text, tables, and appendices while holding competing approaches in mind simultaneously.

When those synthesis decisions are undocumented or inconsistently applied, the consequences are serious. Regulators and HTA bodies expect to trace every parameter back to its evidence base and understand why one methodological approach was chosen over another. Poorly sourced specifications invite challenge, delay market access, and create audit risk that can surface months after the original work was completed.

How It's Done Today

In most organizations, a health economist begins by assembling a set of relevant published models, often five or more articles, and works through them manually, article by article, domain by domain. For each source, they extract parameters from tables and supplementary materials, note methodological choices, and try to build a picture of how different authors have approached the same modeling problem. This extraction phase alone, before any synthesis has begun, typically consumes the better part of a week.

The synthesis phase is where the real complexity sets in. The analyst must work across five core modeling domains (model structure, disease progression, treatment efficacy, costs and utilities, and sensitivity analysis), reconciling how each article approaches each domain and deciding which methods best fit the research question. Where articles disagree, the analyst must make judgment calls and document the rationale. Where articles align, they must verify that the alignment is genuine and not an artifact of different terminology describing different things. This systematic, domain-by-domain cross-referencing is genuinely difficult to sustain across two to four weeks of work. Source attribution is inconsistent. Sections written early in the process may not reflect decisions made later. And the record of why one approach was preferred over another is rarely preserved.

When feedback arrives, whether from an internal review, a co-author, or a regulator, the revision process starts again from scratch. The analyst returns to the source articles, re-reads the relevant sections, and manually updates the specification, hoping that changes in one section do not silently break the logic of another. For organizations running multiple submissions per year, this cycle repeats at a pace that leaves little room for the analytical work that actually requires human judgment.

The AI-Enabled Approach

Model Specifications Developer supports two starting points, depending on where you are in your workflow. You can upload published articles directly, as many as your evidence base requires, and optionally describe your research question to guide the synthesis. Or, if you have already developed a model concept document (for example, using the upstream Concept Design module), you can upload that directly and the system will generate implementation-ready specifications from the structural blueprint you have already defined. Either way, the system takes over the extraction and synthesis work from there.

Working across all uploaded sources simultaneously, the system analyzes each one across five standardized modeling domains: model description, disease progression, treatment efficacy, costs and utilities, and sensitivity analysis. For multi-article inputs, it does not simply summarize each article in isolation. It synthesizes the evidence domain by domain, producing three distinct outputs: a detailed summary of each individual article for future reference, a cross-article comparison that surfaces where sources agree, where they diverge, and what methodological trade-offs are at stake, and a proposed specification document with every parameter and design choice attributed to its source.

The result is not a first draft that needs to be rebuilt. It is a structured, source-attributed specification ready for review and refinement. You can adjust assumptions, explore alternative approaches, or ask questions about why a particular parameter was recommended through a conversational interface. The system identifies which specification sections are affected by your request, retrieves the relevant source context from the original articles, and updates the document consistently. You can also edit the specification directly for small changes you prefer to make by hand. When you are ready, you export to Word or Markdown, with optional model structure diagrams included.

What It Means for You

• What once required two to four weeks of manual reading, extraction, and synthesis now completes in hours, including time for review and interactive refinement.
• You receive three distinct outputs from a single workflow: per-article summaries for future reference, a cross-article comparison showing where sources agree and diverge, and a proposed specification with every parameter attributed to its source.
• The cross-article comparison gives you the documentation HTA reviewers and regulators expect: a transparent record of which methodological approaches were considered, how they differ, and why one was preferred over another.
• Revisions requested after internal review or stakeholder feedback are handled intelligently: the system identifies which specification sections are affected, retrieves the relevant source context from the original articles, and updates the document consistently. Small edits can also be made directly by hand.
• You can start from published articles or from a model concept document. The system adapts its analysis to your starting point, whether you are synthesizing evidence from scratch or translating an existing structural design into implementation-ready detail.
• Analysts spend their time on the decisions that require human judgment, not on manual table extraction, parameter hunting, or cross-referencing hundreds of pages of supplementary materials.