Pediatric Chronic Critical Illness: Gaps in Inpatient Intrateam Communication*
Bibliographic record
Abstract
OBJECTIVES: The number of children with medical complexity and prolonged hospitalizations is rising. Strategies to adapt acute care approaches for this population are falling behind clinical demand. This study aimed to identify how inpatient team communication practices match the needs of teams caring for these patients and families, and to identify priority areas for improvement. DESIGN: Cross-sectional mixed methods survey. SETTING: Academic children's hospital. SUBJECTS: Interdisciplinary healthcare professionals: physicians, nurse practitioners, nurses, resident and fellow trainees, respiratory therapists, clinical pharmacists, occupational therapists, physical therapists, social workers, and child life specialists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Four-hundred eight interdisciplinary healthcare professionals participated (33% response rate). Half (53%) worked in ICUs and 37% had greater than 10 years clinical experience. Three overarching themes emerged regarding communication during care of children with prolonged hospitalizations are as follows: 1) Dysfunctional team collaboration: the many involved healthcare providers for these children have inconsistent team meetings and few platforms for reaching clinical consensus; 2) Continuity gaps: time-limited clinician rotations and no designated longitudinal clinical leaders undermine relationships with families and key elements of shared decision-making; and 3) Inadequate communication skills and tools: healthcare professionals have inadequate training to address complex conversations and big picture concerns, and often default to daily management conversations. Nearly half (40%) perceived intra-team conflict to occur more commonly during care of these children compared with those with short hospitalizations, and many feel unskilled to address these conflicts. Healthcare providers working in ICUs were more likely than other healthcare providers to find care of children with chronic critical illness stressful "most of the time" (ICU 46%; 60/131 vs non-ICU 25%; 21/84; p = 0.02). CONCLUSIONS: Acute care inpatient communication practices require modification to meet the needs of healthcare professionals who provide longitudinal care to children with repeated and prolonged hospitalizations. Improvement strategies should prioritize building collaboration, continuity, and communication skills among healthcare professionals.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".