Central COVID-19 Coordination Centers in Germany: Description, Economic Evaluation, and Systematic Review
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, Central COVID-19 Coordination Centers (CCCCs) have been established at several hospitals across Germany with the intention to assist local health care professionals in efficiently referring patients with suspected or confirmed SARS-CoV-2 infection to regional hospitals and therefore to prevent the collapse of local health system structures. In addition, these centers coordinate interhospital transfers of patients with COVID-19 and provide or arrange specialized telemedical consultations. OBJECTIVE: This study describes the establishment and management of a CCCC at a German university hospital. METHODS: We performed economic analyses (cost, cost-effectiveness, use, and utility) according to the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) criteria. Additionally, we conducted a systematic review to identify publications on similar institutions worldwide. The 2 months with the highest local incidence of COVID-19 cases (December 2020 and January 2021) were considered. RESULTS: During this time, 17.3 requests per day were made to the CCCC regarding admission or transfer of patients with COVID-19. The majority of requests were made by emergency medical services (601/1068, 56.3%), patients with an average age of 71.8 (SD 17.2) years were involved, and for 737 of 1068 cases (69%), SARS-CoV-2 had already been detected by a positive polymerase chain reaction test. In 59.8% (639/1068) of the concerned patients, further treatment by a general practitioner or outpatient presentation in a hospital could be initiated after appropriate advice, 27.2% (291/1068) of patients were admitted to normal wards, and 12.9% (138/1068) were directly transmitted to an intensive care unit. The operating costs of the CCCC amounted to more than €52,000 (US $60,031) per month. Of the 334 patients with detected SARS-CoV-2 who were referred via EMS or outpatient physicians, 302 (90.4%) were triaged and announced in advance by the CCCC. No other published economic analysis of COVID-19 coordination or management institutions at hospitals could be found. CONCLUSIONS: Despite the high cost of the CCCC, we were able to show that it is a beneficial concept to both the providing hospital and the public health system. However, the most important benefits of the CCCC are that it prevents hospitals from being overrun by patients and that it avoids situations in which physicians must weigh one patient's life against another's.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".