Evaluation of a Rural Emergency Medical Service Project in Germany: Protocol for a Multimethod and Multiperspective Longitudinal Analysis
Bibliographic record
Abstract
BACKGROUND: German emergency medical services are a 2-tiered system with paramedic-staffed ambulances as the primary response, supported by prehospital emergency doctors for life-threatening conditions. As in all European health care systems, German medical practitioners are in short supply, whereas the demand for timely emergency medical care is constantly growing. In rural areas, this has led to critical delays in the provision of emergency medical care. In particular, in cases of cardiac arrest, time is of the essence because, with each passing minute, the chance of survival with good neurological outcome decreases. OBJECTIVE: The project has 4 main objectives: (1) reduce the therapy-free interval through widespread reinforcement of resuscitation skills and motivating the public to provide help (ie, bystander cardiopulmonary resuscitation), (2) provide faster professional first aid in addition to rescue services through alerting trained first aiders by mobile phone, (3) make more emergency physicians available more quickly through introducing the tele-emergency physician system, and (4) enhance emergency care through improving the cooperation between statutory health insurance on-call medical services (German: Kassenärztlicher Bereitschaftsdienst) and emergency medical services. METHODS: We will evaluate project implementation in a tripartite prospective and intervention study. First, in medical evaluation, we will assess the influences of various project measures on quality of care using multiple methods. Second, the economic evaluation will mainly focus on the valuation of inputs and outcomes of the different measures while considering various relevant indicators. Third, as part of the work and organizational analysis, we will assess important work- and occupational-related parameters, as well as network and regional indexes. RESULTS: We started the project in 2017 and will complete enrollment in 2020. We finished the preanalysis phase in September 2018. CONCLUSIONS: Overall, implementation of the project will entail realigning emergency medicine in rural areas and enhancing the quality of medical emergency care in the long term. We expect the project to lead to a measurable increase in medical laypersons' individual motivation to provide resuscitation, to strengthen resuscitation skills, and to result in medical laypersons providing first aid much more frequently. Furthermore, we intend the project to decrease the therapy-free interval in cases of cardiac arrest by dispatching first aiders via mobile phones. Previous projects in urban regions have shown that the tele-emergency physician system can provide a higher availability and quality of emergency call-outs in regular health care. We expect a closer interrelation of emergency practices of statutory health insurance physicians with the rescue service to lead to better coordination of rescue and on-call services. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14358.
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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 | 0.082 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".