Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: In the emergency department (ED), health care providers face extraordinary pressures in delivering accurate diagnoses and care, often working with fragmented or inaccessible patient histories while managing severe time constraints and constant interruptions. These challenges and pressures may lead to potential errors in the ED diagnostic process and risks to patient safety. With advances in technology, interventions have been developed to support ED providers in such pressured settings. However, these interventions may not align with the current practices of ED providers. To better design ED provider-centered interventions, identifying their needs in the diagnostic process is critical. OBJECTIVE: This study aimed to identify ED providers' needs in the diagnostic process through participatory design sessions and to propose design guidelines for provider‑centered technological interventions that support decision‑making and reduce errors. METHODS: We conducted a participatory design study with ED providers to validate their needs and identify considerations for designing ED provider-centered interventions to improve diagnostic safety. We used 9 technological intervention ideas as storyboards to address the study participants' needs. We had participants discuss the use cases of each intervention idea to assess their needs during the ED care process and facilitated co-design activities with the participants to improve the technological intervention designs. We audio- and video-recorded the design sessions. We then analyzed session transcripts, field notes, and design sketches. In total, we conducted 6 design sessions with 17 ED frontline providers. RESULTS: Through design sessions with ED providers, we identified 4 key needs in the diagnostic process: information integration, patient prioritization, ED provider-patient communication, and care coordination. We interpreted them as insights for designing technological interventions for ED patients. Hence, we discussed the design implications for technological interventions in four key areas: (1) enhancing ED provider-ED provider communication, (2) enhancing ED provider-patient communication, (3) optimizing the integration of advanced technology, and (4) unleashing the potential of artificial intelligence tools in the ED to improve diagnosis. This work offers evidence-based technology design suggestions for improving diagnostic processes. CONCLUSIONS: This study provides unique insights for designing technological interventions to support ED diagnostic processes. By inviting ED providers into the design process, we present unique insights into the diagnostic process and design considerations for designing novel technological interventions that meet ED providers' needs in the diagnostic process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/55357.
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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 | 0.079 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".