Identifying High-Priority Ethical Challenges for Precision Emergency Medicine: Nominal Group Study
Bibliographic record
Abstract
Background: Precision medicine promises to revolutionize health care by providing the right care to the right patient at the right time. However, the emergency department's unique mandate to treat "anyone, anywhere, anytime" creates critical tensions with precision medicine's requirements for comprehensive patient data and computational analysis. As emergency departments serve as health care's safety net and provide a growing proportion of acute care in America, identifying and addressing the ethical challenges of implementing precision medicine in this setting is crucial to prevent exacerbation of existing health care disparities. The rapid advancement of precision medicine technologies makes it imperative to understand these challenges before widespread implementation in emergency care settings. Objective: This study aimed to identify high priority ethical concerns facing the implementation of precision medicine in the emergency department. Methods: We conducted a qualitative study using a modified nominal group technique (NGT) with emergency physicians who had previous knowledge of precision medicine concepts. The NGT process consisted of four phases: (1) silent generation of ideas, (2) round-robin sharing of ideas, (3) structured discussion and clarification, and (4) thematic grouping of priorities. Participants represented diverse practice settings (county hospital, community hospital, academic center, and integrated managed care consortium) and subspecialties (education, ethics, pediatrics, diversity, equity, inclusion, and informatics) across various career stages from residents to late-career physicians. Results: A total of 12 emergency physicians identified 82 initial challenges during individual ideation, which were consolidated to 48 unique challenges after removing duplicates and combining related items. The average participant contributed 6.8 (SD 2.9) challenges. These challenges were organized into a framework with 3 themes: values, privacy, and justice. The framework identified the need to address these themes across 3 time points of the precision medicine process: acquisition of data, actualization in the care setting, and the after effects of its use. This systematic organization revealed interrelated concerns spanning from data collection and bias to implementation challenges and long-term consequences for health care equity. Conclusions: Our study developed a novel framework that maps critical ethical challenges across 3 domains (values, privacy, and justice) and 3 temporal stages of precision medicine implementation. This framework identifies high-priority areas for future research and policy development, particularly around data representation, privacy protection, and equitable access. Successfully addressing these challenges is essential to realize precision medicine's potential while preserving emergency medicine's core mission as health care's safety net.
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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 | 0.061 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".