A scoping review of new implementations of interprofessional bedside rounding models to improve teamwork, care, and outcomes in hospitals
Bibliographic record
Abstract
Poor communication within healthcare teams occurs commonly, contributing to inefficiency, medical errors, conflict, and other adverse outcomes. Interprofessional bedside rounds (IBR) are a promising model that brings two or more health professions together with patients and families as part of a consistent, team-based routine to share information and collaboratively arrive at a daily plan of care. The purpose of this systematic scoping review was to investigate the breadth and quality of IBR literature to identify and describe gaps and opportunities for future research. We followed an adapted Arksey and O'Malley Framework and PRISMA scoping review guidelines. PubMed, CINAHL, PsycINFO, and Embase were systematically searched for key IBR words and concepts through June 2020. Seventy-nine articles met inclusion criteria and underwent data abstraction. Study quality was assessed using the Mixed Methods Assessment Tool. Publications in this field have increased since 2014, and the majority of studies reported positive impacts of IBR implementation across an array of team, patient, and care quality/delivery outcomes. Despite the preponderance of positive findings, great heterogeneity, and a reliance on quantitative non-randomized study designs remain in the extant research. A growing number of interventions to improve safety, quality, and care experiences in hospital settings focus on redesigning daily inpatient rounds. Limited information on IBR characteristics and implementation strategies coupled with widespread variation in terminology, study quality, and design create challenges in assessing the effectiveness of models of rounds and optimal implementation strategies. This scoping review highlights the need for additional studies of rounding models, implementation strategies, and outcomes that facilitate comparative research.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".