Krankenhauskosten ambulant-sensitiver Krankenhausfälle in Deutschland
Bibliographic record
Abstract
Ambulatory care-sensitive conditions (ACSC) are defined as conditions that lead to a hospital admission of which the onset could have been prevented through a more easily accessible ambulatory sector or one that provides better quality care. They are used by health-care systems as a quality indicator for the ambulatory sector. The definition for ACSC varies internationally. Sets of conditions have been defined and evaluated already in various countries, e. g., USA, England, New Zealand and Canada, but not yet for Germany. Therefore this study aims to evaluate the hospital costs of ACSC in Germany using the National Health Service's set of ACSC. In order to calculate these costs a model has been set up for the time period between 2003 and 2010. It is based on G-DRG browsers issued by the German Institute for the Hospital Remuneration System as required by German law. Within these browsers all relevant DRG-ICD combinations have been extracted. The number of cases per combination was then multiplied by their corresponding cost weights and the average effective base rates. The results were then aggregated into their corresponding ICD groups and then into their respective conditions which lead to the costs per condition and the total costs. The total number of cases and total costs were then compared to another second source. These calculations resulted in 11.7 million cases, of which 10.7% were defined as ambulatory care-sensitive. Within the analysed time period the number of ambulatory care-sensitive cases increased by 6% in total and had a 0.9% CAGR. The corresponding costs amounted to a total of EUR 37.6B and to EUR 3.3B for ACSC. 60% of the costs were caused by three of the 19 ACSC. These results validate that it is worthwhile to further investigate this quality indicator for the ambulatory sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".