Technological incorporation in the Unified Health System (SUS): the problem and ensuing challenges
Bibliographic record
Abstract
Technological incorporation is a central topic among the concerns regarding health care systems. This paper discusses the role of technology dynamics in health systems' cost increases, suggesting two different approaches - a 'pragmatic-economic' approach and a 'rational-defensive' approach - as guidelines to explain the reasons for this centrality. The paper shows how judicialization results from this situation and discusses two doctrinal views - 'reserve for contingencies' and 'rational use' - as the views that usually guide the debates in the courts and among health policy makers. The paper suggests that the attitude currently prevalent in the Brazilian judiciary system can prejudice the principle of equity by improperly evaluating the principle of integrality. We present a brief genealogy of HTA and a timeline of HTA in Brazil. We also discuss the relevance and the impact of Law 12401/2011, which regulates the principle of integrality in the Unified Health System (SUS) and propose three challenges to the development of HTA actions aiming at technology incorporation in Brazil. Finally, we discuss the entry and the role of private health insurance companies, emphasizing changes in the scenario and in their position.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".