Forced Centralized Allocation of Patients to Temporarily ‘Closed’ Emergency Departments
Bibliographic record
Abstract
BACKGROUND: Because insufficient data are available, the overall number of patients treated in German emergency departments can only be estimated. It is evident, however, that case numbers have been rising steadily in recent years, and that a lack of capacity is now leading with increasing freuqency to forced centralized allocation of patients by the emergency medical services (EMS) to emergency departments that are, officially, temporarily "closed". METHODS: Trends in patient allocation of this type in greater Munich, Germany, over the years 2013-2019 were analyzed for the first time on the basis of data from 904 997 cases treated by the emergency rescue services. RESULTS: From 2014 to 2019, the number of forced centralized patient allocations rose approximately by a factor of nine, from 70 to 634 per 100 000 persons per year. In the same period, the overall number of cases treated by the emergency rescue services rose by 14.5%. Peak values for forced centralized allocations were reached in the first quarter of each calendar year (2015: 1579, 2017: 2435, 2018: 3161, 2019: 3990). Of all medical specialties, internal medicine was the most heavily affected (more than 59% of the total). Especially in the years 2017-2019, the free availability of internal medicine declined in hospitals participating in the common greater Munich reporting system. CONCLUSION: The reasons for the sharp rise in forced centralized allocations are unclear. This observed trend seems likely to persist over the coming years, in view of the current staff shortage, the aging population, and diminishing hospital capacities. The relevant decision-makers must collaborate to create emergency plans that will prevent care bottlenecks so that patients will not be endangered.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".