Why English? | Reporting ELEVATE-GenAI Health Economics Research Through Model Details, Accuracy, Reproducibility, Fairness and Human Review

ORIENTATION

What this guide owns

Current fact check

ELEVATE-GenAI appeared in Value in Health 28(11), pages 1611–1625, in November 2025 (DOI 10.1016/j.jval.2025.06.018; PubMed 40653157). The framework has ten domains and was illustrated using two HEOR case studies.

Canonical boundary

This article owns reporting of LLM-assisted health economics and outcomes research. CHEERS-AI owns full economic evaluations of AI interventions, GAMER owns general generative-AI disclosure and PRISMA-trAIce owns AI used as a method in systematic reviews.

Worked example

Every chapter returns to a fictional health-economic study using an LLM to help identify utility values and resource-use inputs for a school-based asthma programme model. It is fictional, low-stakes and educational; it does not report a real school, clinical decision or guaranteed outcome.

Warm up with eduKate’s guides to explaining scientific evidence and asking better questions in class.

CHAPTER 1 OF 16

1. Name the HEOR Question and Decision Context

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Why this move matters

State population, intervention, comparator, outcomes, perspective, time horizon and who may use the evidence.

Start with the HEOR decision

State population, intervention, comparator, outcomes, perspective, time horizon and who may use the evidence. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The study estimates value from a health-system perspective without claiming a reimbursement decision. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A clear question does not guarantee the selected model structure is appropriate. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Name the HEOR Question and Decision Context”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 2 OF 16

2. Describe the LLM Role With Concrete Verbs

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Why this move matters

Say whether the model searched, screened, extracted, classified, drafted code, generated scenarios or edited prose.

Start with the HEOR decision

Say whether the model searched, screened, extracted, classified, drafted code, generated scenarios or edited prose. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The LLM proposes candidate utility values and code comments; humans verify and enter final inputs. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A precise verb does not prove the task was done well. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Describe the LLM Role With Concrete Verbs”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 3 OF 16

3. Identify the Model, Access and Evaluation Window

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Why this move matters

Report provider, model, version, access route, dates, settings and any retrieval or tools.

Start with the HEOR decision

Report provider, model, version, access route, dates, settings and any retrieval or tools. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The team records the hosted model label, API date range and retrieval source list. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A dated label cannot expose every server-side change. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Identify the Model, Access and Evaluation Window”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 4 OF 16

4. Explain Why an LLM Was Used

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Why this move matters

Give the intended benefit, alternatives considered, risks, costs and reason the task suited language-model assistance.

Start with the HEOR decision

Give the intended benefit, alternatives considered, risks, costs and reason the task suited language-model assistance. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The team uses the model to organise candidate sources, not to replace economic judgement. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Efficiency claims need measurement rather than assumption. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Explain Why an LLM Was Used”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 5 OF 16

5. Trace Data and Source Provenance

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Why this move matters

List literature, databases, documents, tables and real-world data entering the workflow, including permissions and dates.

Start with the HEOR decision

List literature, databases, documents, tables and real-world data entering the workflow, including permissions and dates. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Each utility estimate keeps a citation, population, instrument and extraction location. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A correct extraction from a poor source remains poor evidence. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Trace Data and Source Provenance”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 6 OF 16

6. Preserve Prompts, Examples and Retrieval Rules

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Why this move matters

Report system messages, task prompts, examples, document selection, chunking, search logic and output schemas.

Start with the HEOR decision

Report system messages, task prompts, examples, document selection, chunking, search logic and output schemas. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The prompt asks for source quotations, units, population and uncertainty in a fixed table. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A strict schema can still contain plausible false values. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Preserve Prompts, Examples and Retrieval Rules”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 7 OF 16

7. Separate Development, Testing and Production Use

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Why this move matters

Show which records tuned prompts, which tested accuracy and which entered the final analysis.

Start with the HEOR decision

Show which records tuned prompts, which tested accuracy and which entered the final analysis. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Prompt examples come from pilot papers; a separate verified set evaluates extraction. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A small held-out set may not represent the full literature. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Separate Development, Testing and Production Use”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 8 OF 16

8. Document Human Expertise and Review

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Why this move matters

Name reviewers, training, independence, adjudication and which decisions remained human.

Start with the HEOR decision

Name reviewers, training, independence, adjudication and which decisions remained human. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: A health economist and clinician independently verify candidate inputs and resolve disagreements. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Expert review can still share systematic assumptions or miss errors. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Document Human Expertise and Review”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 9 OF 16

9. Validate Accuracy at the Field Level

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Why this move matters

Compare extracted values, citations, units, populations and qualifiers against source documents using task-specific metrics.

Start with the HEOR decision

Compare extracted values, citations, units, populations and qualifiers against source documents using task-specific metrics. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The audit scores exact value, correct unit, source support and context separately. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

One aggregate accuracy number can hide dangerous unit or population errors. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Validate Accuracy at the Field Level”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 10 OF 16

10. Test Reproducibility and Output Variation

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Why this move matters

Repeat a sample, report stochastic settings, compare runs and preserve raw outputs and transformation logs.

Start with the HEOR decision

Repeat a sample, report stochastic settings, compare runs and preserve raw outputs and transformation logs. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Repeated extractions reveal stable values but inconsistent rationales, which the report documents. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Repetition does not reveal errors shared across every run. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Test Reproducibility and Output Variation”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 11 OF 16

11. Show How LLM Errors Affect the Economic Model

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Why this move matters

Propagate plausible extraction or coding errors through costs, outcomes and incremental results.

Start with the HEOR decision

Propagate plausible extraction or coding errors through costs, outcomes and incremental results. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Sensitivity analysis tests a misread time horizon and an incorrect unit conversion. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Not every language-model error can be represented by one parameter range. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Show How LLM Errors Affect the Economic Model”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 12 OF 16

12. Report Traditional Model Uncertainty Separately

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Why this move matters

Distinguish LLM uncertainty from sampling, parameter, structural and methodological uncertainty in the HEOR analysis.

Start with the HEOR decision

Distinguish LLM uncertainty from sampling, parameter, structural and methodological uncertainty in the HEOR analysis. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Probabilistic sensitivity analysis remains separate from repeated LLM outputs. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Language-model variability is not a probability distribution over true economic values. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Report Traditional Model Uncertainty Separately”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 13 OF 16

13. Evaluate Fairness, Bias and Representation

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Why this move matters

Ask whose language, evidence, outcomes and settings are missing and whether errors differ across populations or document types.

Start with the HEOR decision

Ask whose language, evidence, outcomes and settings are missing and whether errors differ across populations or document types. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The study checks whether non-English abstracts and lower-resource settings are systematically overlooked. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Small subgroup samples limit strong fairness conclusions. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Evaluate Fairness, Bias and Representation”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 14 OF 16

14. Protect Privacy, Confidentiality and Intellectual Property

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Why this move matters

Report what entered external services, de-identification, retention, approved environments, licences and restrictions.

Start with the HEOR decision

Report what entered external services, de-identification, retention, approved environments, licences and restrictions. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: Only public articles and synthetic model inputs enter the hosted system. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Vendor controls do not replace consent, law or organisational policy. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Protect Privacy, Confidentiality and Intellectual Property”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 15 OF 16

15. Report Time, Cost and Workflow Effects

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Why this move matters

Measure human hours, review burden, service cost, failures and the comparison with a non-LLM process.

Start with the HEOR decision

Measure human hours, review burden, service cost, failures and the comparison with a non-LLM process. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The report gives reviewer time saved and additional verification time required. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

A faster pilot may not remain cheaper at scale. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Report Time, Cost and Workflow Effects”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

CHAPTER 16 OF 16

16. Write a Policy-Safe Conclusion

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Why this move matters

Connect claims to the validated task and case study, disclose remaining tests and avoid implying a reimbursement or resource-allocation decision.

Start with the HEOR decision

Connect claims to the validated task and case study, disclose remaining tests and avoid implying a reimbursement or resource-allocation decision. Name the health-economic or outcomes-research task, users, evidence need and decision before describing the language model.

Trace the economic evidence workflow

Worked example: The conclusion supports further validation of assisted extraction, not adoption of the asthma programme. Show sources, prompts, human review, extracted quantities, model calculations, validation and sensitivity analysis.

Connect accuracy to consequences

Explain how an error could change evidence selection, resource use, costs, outcomes or equity. Pair technical checks with domain-expert review.

State transfer and policy limits

Transparent reporting cannot repair weak underlying evidence. ELEVATE-GenAI improves transparency in HEOR; it cannot make an uncertain model output a valid cost-effectiveness result or policy recommendation.

Student, parent and career transfer

Make one evidence card for “Write a Policy-Safe Conclusion”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health economics, outcomes research, public policy, evidence synthesis, modelling, healthcare consulting and research communication without promising a grade, admission, funding decision or job.

PRACTICE LAB

Build a elevate decision trail

Student route

1. Choose one original report inside this guide’s scope.
2. Copy its objective and underline actor, action, context, evidence and intended decision.
3. Draw the specific reasoning chain used in this article.
4. Mark each step reported, inferred, contested or unknown.
5. Rewrite the conclusion so it preserves the narrowest important limitation.

Parent or teacher route

Ask the learner to explain explain how an llm changed health-economic evidence, then test every handover from source to decision twice: once to a classmate and once to a method specialist. Praise a precise correction and a well-stated unknown; both show disciplined attention to evidence.

Early-career route

Create a one-page elevate audit with source link, date checked, scope, method logic, decisive wording, contrary evidence, limit and next action. A colleague should be able to continue without guessing.

Continue with eduKate’s guides to reading course and career information and giving useful feedback in group projects.

USEFUL NEXT READING

Connect this narrow owner to its neighbour

Read the REFORMS reproducible machine-learning guide for a related but different reader job. Return here when the question is specifically how to explain how an llm changed health-economic evidence, then test every handover from source to decision. This boundary keeps one canonical owner per intent.

For English practice across research, school and career communication, visit the English at work and in public life guide.

FREQUENTLY ASKED QUESTIONS

Questions students and parents often ask

What does HEOR mean?

Health economics and outcomes research.

How many domains are in ELEVATE-GenAI?

The framework contains ten domains.

Does it replace CHEERS?

No. ELEVATE-GenAI reports LLM use in HEOR; CHEERS reports health economic evaluations.

Why validate units and populations separately?

A correct number can be wrong for the model if its unit, time horizon or population differs.

Is repeated LLM output the same as probabilistic sensitivity analysis?

No. They represent different sources of variation.

Did the authors claim the framework was final?

They called for further empirical testing of validity, completeness, usability and generalisability.