A qualitative study of emergency physicians’ perspectives on PROMS in the emergency department
Bibliographic record
Abstract
INTRODUCTION: There is a growing emphasis on including patients' perspectives on outcomes as a measure of quality care. To date, this has been challenging in the emergency department (ED) setting. To better understand the root of this challenge, we looked to ED physicians' perspectives on their role, relationships and responsibilities to inform future development and implementation of patient-reported outcome measures (PROMs). METHODS: ED physicians from hospitals across Canada were invited to participate in interviews using a snowballing sampling technique. Semistructured interviews were conducted by phone with questions focused on the role and practice of ED physicians, their relationship with their patients and their thoughts on patient-reported feedback as a mechanism for quality improvement. Transcripts were analysed using a modified constant comparative method and interpretive descriptive framework. RESULTS: Interviews were completed with 30 individual physicians. Respondents were diverse in location, training and years in practice. Physicians reported being interested in 'objective' postdischarge information including adverse events, readmissions, other physicians' notes, etc in a select group of complex patients, but saw 'patient-reported' feedback as less valuable due to perceived biases. They were unsure about the impact of such feedback mainly because of the episodic nature of their work. Concerns about timing, as well as about their legal and ethical responsibilities to follow-up if poor patient outcomes are reported, were raised. CONCLUSIONS: Data collection and feedback are key elements of a learning health system. While patient-reported outcomes may have a role in feedback, ED physicians are conflicted about the actionability of such data and ethical implications, given the inherently episodic nature of their work. These findings have important implications for PROM design and implementation in this unique clinical setting.
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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 | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".