Level 1 · Foundations
Framing the Economic Question for AI
Session 2 told you which tools to reach for. This session tells you how to phrase the question so those tools return health economics evidence rather than clinical trial summaries.
When health economists and financing specialists turn to AI tools for literature support, they often get back clinical trial summaries, drug efficacy tables, or biomedical findings — none of which is what they came for. The reason is almost always the same: the question was framed in clinical language, so the AI searched clinical space.
The framework you use to pose your research question determines what the AI finds. This session introduces a question structure designed specifically for health economics and financing work, and shows you how to translate it into AI prompts that serve WHO India's mandate.
The standard clinical framework is PICO: Population, Intervention, Comparator, Outcome. It works well for asking whether Drug A beats Drug B in a randomised trial. It does not work well for asking whether community health insurance schemes reduce catastrophic health expenditure among informal workers — which is closer to the kind of question this team actually works on.
For health financing and priority-setting, the more useful frame is PECO-F:
| Element | What it means | India / LMIC example |
|---|---|---|
| P — Population | Who bears the burden? | Informal sector households in Uttar Pradesh |
| E — Exposure / Intervention | What financing mechanism or policy? | Contributory health insurance vs. tax-funded coverage |
| C — Comparator | What is the alternative? | No coverage / out-of-pocket only |
| O — Outcome | What do we measure? | Catastrophic health expenditure, impoverishment, utilisation rates |
| F — Feasibility & Equity | What constraints apply? | Affordability, gender equity, willingness to pay, political acceptability |
The F element is not cosmetic. The acceptability and equity dimensions of an intervention can overturn a purely cost-effectiveness ranking — a mass-media health intervention may score poorly on cost-per-DALY but rank highly on acceptability with local stakeholders and on distributional grounds. AI tools will not surface that tension unless you ask for it explicitly.
The second prompt also signals the methodological register — systematic reviews, economic evaluations — so the tool does not return qualitative ethnographies or clinical guidelines.
When the question is about allocating scarce resources across competing interventions, the frame shifts slightly. The question is no longer just "does this work?" but "should this be funded, given competing claims on the same budget?" For these questions, add two further dimensions:
That prompt navigates the ACE (Assessing Cost-Effectiveness) and A4R (Accountability for Reasonableness) literatures simultaneously, which is exactly where India UHC reform work sits.
The embedded app below covers the practical workflow for Sessions 02 and 03 — specifically how PECO-F framing shapes what you search for and how you extract from what you find. It uses a real HTA context: CAD4TB chest X-ray AI evaluation across 18 PHC sites in India. Use the Live Demo button inside the app for a guided 10-step walkthrough, or navigate directly to Session 02 or 03.
Type a research question your team is currently working on. The tool below will reframe it using PECO-F and flag the equity and feasibility dimensions you should add to your AI prompt.
The framework is not bureaucracy — it is precision. A structured question reduces the chance that AI returns the wrong kind of evidence. For WHO staff working on UHC, financing, and priority-setting, that means explicitly naming populations, mechanisms, comparators, outcomes, and equity constraints every time. Session 4 shows you how to encode this into a reusable search protocol — so the structure becomes automatic rather than effortful.