71 Barriers to providing optimal care to children with medical complexity in the emergency department: A physicians' perspective
Bibliographic record
Abstract
Abstract Background Children with medical complexity (CMC) are a subset of the pediatric population with chronic medical conditions requiring specialized care and high health resource utilization. Due to the nature of their underlying diseases, along with the medical devices that they are reliant on, they account for a large proportion of annual pediatric emergency department (ED) visits in tertiary centres and about one third of the children admitted. ED physicians are often the first to interact with CMC patients and thus the quality of care they provide influences the hospital course of those patients. Objectives To determine and explore the barriers that ED physicians face when providing care to CMC in the ED and identify potential areas of improvement in care provided in the ED. Design/Methods Semi-structured personal interviews were conducted virtually with 20 pediatric emergency medicine physicians and fellows. Each participant was asked the same set of questions and given extra time to elaborate. Interviews were transcribed verbatim and manual coding was conducted to identify commonalities across the interviews. Results We identified four major themes and 12 sub-themes as barriers to optimal care of CMC in the ED. Major themes included time, training, medico-legal concerns, and care plans. The two most commonly identified barriers were time required to care for CMC patients, identified by 90% (n=18) of the participants, and limited exposure during fellowship training, identified by 80% (n=16) of the participants. Only 20% (n=4) identified medico-legal challenges as a unique barrier when managing CMC. All the participants (n=20) emphasized the importance of a care plan, a detailed document given to caregivers of CMC by the primary care team with details about their medical condition, and if not available, 85% (n=17) indicated that it will affect the efficiency and quality of care provided. Even if a care plan is available, 50% (n=10) of the participants would still contact the complex care team for further management recommendations. Conclusion Care plan accessibility and time restraint in the ED are major barriers faced by PEM physicians in the ED when caring for CMC. Additional training during fellowship in complex care medicine and providing patients with an updated care plan are two interventions suggested to optimize care of CMC in the ED.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".