MétaCan
Menu
Back to cohort
Record W3198350570 · doi:10.1080/21548331.2021.1977561

The recent evolution of patient care rounds in pediatric teaching hospitals in the United States and Canada

2021· review· en· W3198350570 on OpenAlexaboutno aff
Jeffrey Van Blarcom, Andrew Chevalier, Benjamin Drum, Sarah Eyberg, Elizabeth Vukin, Brian Good

Bibliographic record

VenueHospital Practice · 2021
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsRoundingMedicinePatient safetyScheduleNursingComputer scienceHealth carePolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: National trends toward empowering and enabling patients and families to take a bigger role in their own medical care and enhanced collaboration between rounding stakeholders have effectuated a new rounding model in the pediatric inpatient setting known as 'Patient- and Family-Centered Rounds/I-PASS,' which has shown to decrease safety events and to improve stakeholders' experience with rounding. Other enhancements to the new model, such as the use of whiteboards, rounding checklists, and facecards, have all been applied to the new model to good effect. Another major enhancement to rounding of late has been the application of a schedule to rounds, which has increased the presence of the nurse and the family during rounds and has improved rounding efficiency without a negative effect on teaching. OBJECTIVE: We provide a review of the literature regarding this new rounding model and its effects in the pediatric inpatient setting, as well as a review of the enhancements that have been applied to the new model, the recognized barriers to the implementation of these rounding alterations and the ways in which those barriers have been overcome. CONCLUSIONS: In the pediatric inpatient setting, the 'Patient and Family-Centered Rounds/IPASS' rounding model, as well as enhancements to this new model such as rounding schedules, whiteboards, checklists and facecards, have had a positive effect on stakeholders' experience with rounding, increased patient safety and improved rounding efficiency. Given these positive effects, these alterations to rounding should be promoted and sustained. PLAIN LANGUAGE SUMMARY: Rounding is when a medical care provider, or a team of providers, visits patients in the hospital in order to determine a plan of care and discuss that care with the patient and the patient's family. In teaching hospitals, this involves staff physicians, medical trainees and advanced practice providers. Rounding has changed in the recent past as evolving pressures have increasingly led these teams of providers to talk and make decisions about patients outside the patient's room, which lessens the patient's ability to contribute to decision-making. This also lessens the ability of the patient's nurse to contribute. The recognition of this problem has led to big changes in rounding in children's teaching hospitals, the biggest of which is called 'family-centered rounding.' This involves performing the entirety of rounds in the patients' rooms, directing the discussion toward them in language that they understand, with the active participation of everyone present, including the patient's nurse. Other changes in rounding, designed to improve patients' experiences and decrease medical errors, have made this new rounding model even better. Structured communication during rounds, communication aids such as whiteboards and checklists, and planned times for rounding on each patient ('scheduled rounding') have all successfully been used to improve patients' care and experience in the hospital. This article aims to inform the reader about family-centered rounds and other recent rounding transformations that have proven to increase patient safety and improve their experience while in the hospital, also noting barriers to these changes and how they have been overcome.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.336
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHospital PracticeSame topicInnovations in Medical EducationFrench-language works237,207