Information Mode–Dependent Success Rates of Obtaining German Medical Informatics Initiative–Compliant Broad Consent in the Emergency Department: Single-Center Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: The broad consent (BC) developed by the German Medical Informatics Initiative is a pivotal national strategy for obtaining patient consent to use routinely collected data from electronic health records, insurance companies, contact information, and biomaterials for research. Emergency departments (EDs) are ideal for enrolling diverse patient populations in research activities. Despite regulatory and ethical challenges, obtaining BC from patients in ED with varying demographic, socioeconomic, and disease characteristics presents a promising opportunity to expand the availability of ED data. OBJECTIVE: This study aimed to evaluate the success rate of obtaining BC through different consenting approaches in a tertiary ED and to explore factors influencing consent and dropout rates. METHODS: A single-center prospective observational study was conducted in a German tertiary ED from September to December 2022. Every 30th patient was screened for eligibility. Eligible patients were informed via one of three modalities: (1) directly in the ED, (2) during their inpatient stay on the ward, or (3) via telephone after discharge. The primary outcome was the success rate of obtaining BC within 30 days of ED presentation. Secondary outcomes included analyzing potential influences on the success and dropout rates based on patient characteristics, information mode, and the interaction time required for patients to make an informed decision. RESULTS: Of 11,842 ED visits, 419 patients were screened for BC eligibility, with 151 meeting the inclusion criteria. Of these, 68 (45%) consented to at least 1 BC module, while 24 (15.9%) refused participation. The dropout rate was 39.1% (n=59) and was highest in the telephone-based group (57/109, 52.3%) and lowest in the ED group (1/14, 7.1%). Patients informed face-to-face during their inpatient stay following the ED treatment had the highest consent rate (23/27, 85.2%), while those approached in the ED or by telephone had consent rates of 69.2% (9/13 and 36/52). Logistic regression analysis indicated that longer interaction time significantly improved consent rates (P=.03), while female sex was associated with higher dropout rates (P=.02). Age, triage category, billing details (inpatient treatment), or diagnosis did not significantly influence the primary outcome (all P>.05). CONCLUSIONS: Obtaining BC in an ED environment is feasible, enabling representative inclusion of ED populations. However, discharge from the ED and female sex negatively affected consent rates to the BC. Face-to-face interaction proved most effective, particularly for inpatients, while telephone-based approaches resulted in higher dropout rates despite comparable consent rates to direct consenting in the ED. The findings underscore the importance of tailored consent strategies and maintaining consenting staff in EDs and on the wards to enhance BC information delivery and consent processes for eligible patients. TRIAL REGISTRATION: German Clinical Trials Register DRKS00028753; https://drks.de/search/de/trial/DRKS00028753.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".