A Beginner’s Guide to Writing for Health Economics and Outcomes Research
Bibliographic record
Abstract
The rising costs of health care have prompted many organizations and payers to adopt value-based approaches in deciding which health inventions justify the expenditure, in which patient, and at what cost. Health economics and outcomes research (HEOR) evaluates the economic impact of health interventions and their patient-related outcomes. HEOR has long been used by international health technology assessment (HTA) bodies and US payors to inform reimbursement recommendations. Recent trends suggest that opportunities for medical writers in HEOR projects are likely to continue growing. These trends include the inclusion of real-world evidence (RWE) in the “totality of evidence” supporting regulatory approvals in the United States,1 Canada,2 and Europe3; the use of artificial intelligence to accelerate insights from electronic health records; and the application of HEOR to personalized medicine.4 The growing importance of HEOR evidence and the emergence of new regulatory frameworks will result in increased HEOR outputs and opportunities for medical writers to assist in these projects. This paper reviews opportunities for medical writers in HEOR projects and provides suggestions for medical writers who are working on a manuscript for an HEOR journal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.205 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.100 | 0.099 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".