Designing an App to Overcome Language Barriers in the Delivery of Emergency Medical Services: Participatory Development Process
Bibliographic record
Abstract
BACKGROUND: In emergencies, language barriers may have dangerous consequences for the patients. There have been some technical approaches to overcome language barriers in medical care but not yet in the prehospital emergency care setting. The use of digital technologies in health care is expanding rapidly. Involving end users at all stages of the development process may help to ensure such technologies are usable and can be implemented. OBJECTIVE: We aimed to develop a digital communication tool that addresses paramedic needs in the specific circumstances of prehospital emergency care and helps paramedics to overcome language barriers when providing care to foreign-language patients. METHODS: We actively engaged paramedics and software designers in an action-oriented, participatory, iterative development process, which included field observations, workshops, background conversations, questionnaires on rescue missions, studying the literature, and preliminary testing in the field. RESULTS: With input from paramedics, we created an app with 600 fixed phrases supporting 18 languages. The app includes medical history-taking questions, phrases asking for consent, and phrases providing specific additional information. Children as patients, as well as their carers and other third parties, can be addressed with appropriate wording. All phrases can be played back audibly or displayed as text. The comprehensive content is grouped into categories and adapted to diverse scenarios, which makes the tool rapidly usable. The app includes a function to document patient responses and the conversation history. For evaluation in a clinical study, the app is run on a smartphone with extra speakers to be of use in noisy environments. The use of prototypes proved valuable to verify that the content, structure, and functions discussed in theory were of value and genuinely needed in practice and that the various device control elements were intuitive. CONCLUSIONS: The nature of the paramedic work environment places specific demands on the communication options used and need for such devices. The active involvement of paramedics in the development process allowed us to understand and subsequently consider their experience-based knowledge. Software designers could understand the paramedics' work environment and consider respective needs in the menu navigation and design principles of the app. We argue that the development of any medical software product should actively involve both end users and developers in all phases of the development process. Providing the users with the opportunity to influence technology development ensures that the result is closer to their needs, which can be seen as crucial for successful implementation and sustainable use. TRIAL REGISTRATION: German Clinical Trials Register DRKS00016719; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00016719. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12913-020-05098-5.
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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 | 0.057 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".