Characterization and Evolution of Avoidable Admissions in Portugal: The Impact of Two Methodologic Approaches
Bibliographic record
Abstract
Introduction: The aim of this study is to evaluate the health systems performance through the avoidable hospital admissions, once these have gained international relevance. We used two different methods to identify the admissions for Ambulatory Care Sensitive Conditions, describing the Portuguese reality and evolution.Material and Methods: Over 12 million hospitalizations were analyzed between 2000 and 2012 using the national hospital discharge databases. We used two different methodologies to identify the hospitalizations for Ambulatory Care Sensitive Conditions, determining their concordance. We also estimated potential improvement scenarios.Results: In 2012, 4.4% and 32.4% of the hospitalizations for medical causes were avoidable according to the Canadian and Spanish methodologies respectively. The hospitalizations are more frequent in children and the elderly. The most frequent causes vary according to the age group and methodology. During the analyzed period the rate of admissions has dropped 20% according to the Canadian methodology and increased 16% according to the Spanish methodology. There are regional clusters of performance under and above the national average. The concordance between methodologies is low. The improvement scenarios estimated possible reductions between 20.3% and 53.5% of the hospitalizations.Discussion: The avoidable admissions assume a relevant volume in Portugal. Although in theory they are avoidable their complete elimination is a practical impossibility. Their study, however, allows the evaluation and results motorization enabling to establish intervention priorities.Conclusion: To have a precise characterization of the avoidable admissions in Portugal it is necessary to achieve consensus on the identification methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".