Reinversión en sanidad: fundamentos, aclaraciones, experiencias y perspectivas
Bibliographic record
Abstract
En estos tiempos de crisis económica aumenta mucho más la presión por reducir el gasto como medida aislada que por aplicar fórmulas para maximizar la eficiencia de los servicios sanitarios. Disponemos de información, métodos y experiencias para obtener mejores resultados en salud con los recursos disponibles. En varios países se han adoptado diversas medidas para hacerlo. Una de ellas es la reinversión (también conocida como desinversión). Al tratarse de una táctica necesaria, pero compleja, alergénica y a menudo confundida, en este artículo se aclara su significado, se enmarca en su debido contexto y se describen los métodos y criterios empleados para identificar y priorizar las tecnologías médicas candidatas a la reinversión. Incluido el caso de España, se revisan las experiencias en reinversión de Nueva Zelanda, Australia, Canadá, Reino Unido e Italia, los obstáculos que afrontan y sus perspectivas a medio plazo. El desconocimiento no debería eximir socialmente de su aplicación, estemos o no en crisis. La mejora de la eficiencia social es una obligación del Sistema Nacional de Salud. During the economic crisis, the pressure to reduce health services expenditure as an isolated measure is greater than measures intended to increase the efficiency of these services. Information, methods and experiences to improve health outcomes with limited resources are available and a number of countries have been applying measures to achieve this goal. One of these measures is disinvestment. Given that this tactic is necessary but also intricate, allergenic and confusing, this article tries to clarify its meaning, place it in its correct context, and describe the methods and criteria used to identify and prioritize candidate medical technologies for disinvestment. The experiences of Spain, New Zealand, Australia, Canada, the United Kingdom and Italy in this endeavor are reviewed, as well as the obstacles faced by these countries when disinvesting and their mid-term perspectives. Ignorance does not excuse its application, regardless of whether there is a crisis or not. Efforts to improve social efficiency are a permanent obligation of the national health system.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".