Kompensation von Leistungen zwischen Hausärzten und Kinder- und Jugendmedizinern am Beispiel der Früherkennungsuntersuchungen bei Kindern und Jugendlichen – Analyse von bundesweiten KBV-Daten
Bibliographic record
Abstract
BACKGROUND: Despite a 13.1% increase in the number of pediatricians between 2011 - 2020, the capacity of pediatric care has largely stagnated. This is due to increasing flexibility in working hours and a declining willingness of doctors to establish practices. In addition, there is an imbalance in the distribution of pediatric medical care capacities. While metropolitan areas are often characterized by oversupply, there is an increasing shortage of pediatricians, especially in rural areas. As a result, general practitioners in rural areas are increasingly taking over part of pediatric care. We quantify this compensation effect using the example of examinations of general health and normal child development (U1-U9). METHODS: Basis of the analysis was the Doctors' Fee Scale within the Statutory Health Insurance Scheme (Einheitlicher Bewertungsmaßstab, EBM) from 2015 (4th quarter). Nationwide data from the National Association of Statutory Health Insurance Physicians (KBV) for general practitioners and pediatricians from 2015 was evaluated. In the first step, the EBM was used to determine the potential overlap of services between the two groups of doctors. The actual compensation between the groups was quantified using general health and normal child development as an example. RESULTS: In section 1.7.1 (early detection of diseases in children) of the EBM, there is a list of 16 options for services that can be billed (fee schedule positions, GOP) by general practitioners and pediatricians. This particularly includes child examinations U1 to U9. The analysis of the national data of the KBV for the early detection of diseases in children showed significant differences between rural and urban regions in the billing procedure. Nationwide, general practitioners billed 6.6% of the services in the area of early detection of diseases in children in 2015. In rural regions this share was 23% compared to 3.6% in urban regions. The analysis of the nationwide data showed that the proportion of services billed by general practitioners was higher in rural regions than in urban regions. CONCLUSION: The EBM allows billing of services by both general practitioners and pediatricians, especially in the area of general GOP across all medical groups. The national billing data of the KBV shows that general practitioners in rural regions bill more services from the corresponding sections than in urban regions.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".