Terminservicestellen für die fachärztliche Terminvermittlung – Wie wirksam sind sie wirklich?
Bibliographic record
Abstract
INTRODUCTION: To reduce outpatient specialist waiting times and to help patients with statutory health insurance to get appointments and urgent referrals within four weeks, phone appointment service centres (ASC) were introduced in Germany in January 2016. The aim of this study was to analyse these booking patterns in the Westphalia-Lippe (WL) region, and to compare the types of regular specialist referrals with those made by the centers. Furthermore, neurology services to patients with ASC referrals were compared to those without. METHODS: Appointment data from the second quarter of 2016 to the third quarter of 2019 were used, and an algorithm was developed to determine the range of services provided in appointments made by the ASCs. A total of 24,286,157 accounting slips were compared with 12,648 specialist service records from the Association of Statutory Health Insurance Physicians in the WL region. RESULTS: The average waiting time for an appointment with a specialist was 21 days for 84% of the callers with a referral (aged mostly 35-59 years). Requests for appointments with neurologists, internists, and radiologists were the most frequent ones; 45% of service centre specialist appointments were made with neurologists, despite these comprising only 4% of total referrals in WL. There were only a few differences in the use of services in neurologist appointments with and without the mediation of the ASC. The higher level of ASC used for making neurologist appointments for initial psychotherapeutic assessment was statistically significant. However, the effect was small. CONCLUSIONS: Despite its relatively low use (0.19% of specialist referrals in general), ASCs in the WL region are able to make urgent specialist appointments for patients with statutory insurance, with average waiting times significantly lower than the legally set maximum waiting period. However, patients also take other factors into account when making appointments. While the benefits of these centres, especially for three types of specialists, was demonstrated, further discussion on the form of the ASCs in their current form is warranted. This paper provides a basis for evaluation of methodology and content for further decision-making.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".