Overcoming Language Barriers in Paramedic Care With an App Designed to Improve Communication With Foreign-Language Patients: Nonrandomized Controlled Pilot Study
Bibliographic record
Abstract
BACKGROUND: Communication across language barriers is a particular challenge for health care providers. In emergency medical services, interpreters are mostly not available on rescue scenes, which jeopardizes safe and high-quality medical care. In a cocreative process together with paramedics and software designers, we developed a fixed-phrase translation app with 600 phrases and 18 supported languages that supports paramedics when providing care to foreign-language patients. This paper reports on the results of a pilot study to evaluate the app's effect on paramedic-patient communication. OBJECTIVE: This study aims to gain insights into the efficacy and feasibility of a multilingual app that helps paramedics to communicate with patients who are not proficient in the local language. METHODS: A 3-armed nonrandomized interventional pilot study was conducted in 4 rescue stations in the German Federal State of Lower Saxony: 3 rural areas and 1 in urban environment. The intervention group comprised rescue missions with patients with limited German language proficiency (LGP) with whom the app was used; control group 1 comprised LGP patients without app usage; and control group 2 consisted of rescue missions with German-speaking patients. For each rescue operation with LGP patients, paramedics filled out questionnaires about the communications with patients. From standardized Rescue Service Case Protocols, we extracted information on patient demographics (age and sex), clinical aspects (preliminary diagnosis and Glasgow Coma Scale), and rescue operation characteristics (time spent on emergency scene and additional dispatch of emergency physicians). The primary outcome was the paramedics' perceived quality of communication with LGP patients. The secondary outcome was the ability to obtain necessary information from patients and the ability to provide important information to patients. A linear regression model was applied to assess the impact of the app on perceived communication, controlling demographic factors, and severity of illness. RESULTS: A total of 22 LGP patients were recruited into the intervention group and 112 into control group 1. The control group 2 included 23,045 German-speaking patients. LGP patients were more than 2 decades younger than German-speaking patients. App usage among LGP patients was associated with higher perceived overall quality of communication (0.7 points on a 5-point Likert scale, P=.03). Applying a linear regression model controlling for age, sex, and Glasgow Coma Scale, the quality of communication was associated with an increase of 0.9 points (95% CI 0.2-1.6, P=.01). Compared to either German-speaking patients or LGP patients, paramedics spent 6-7 minutes longer on an emergency scene when the app was used (P=.24). CONCLUSIONS: The use of the app suggests a relevant improvement in communication with patients with limited proficiency in the locally spoken language in paramedic care. The small sample size and the lack of randomization reduce the generalizability of the findings. TRIAL REGISTRATION: German Clinical Trials Register DRKS00016719; https://drks.de/search/de/trial/DRKS00016719.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".