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Record W2762462165 · doi:10.1093/pch/pxx086.074

MULTIDISCIPLINARY TEAM-BASED DELIBERATE PRACTICE USING IN SITU SIMULATIONS TO ENHANCE PATIENT SAFETY ON A PEDIATRIC INPATIENT UNIT

2017· article· en· W2762462165 on OpenAlexaboutno aff
Kristen Lambrinakos-Raymond, David D’Arienzo, M Dandavino, Nadine Korah, V Ballenas, Mubarika Alavi

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsDebriefingPatient safetyContext (archaeology)MedicineMultidisciplinary approachIncident reportMedical emergencyUnit (ring theory)NursingFamily medicineMedical educationPsychologyHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Nearly one in ten children admitted to a Canadian pediatric hospital experience one or more adverse events, many of which are preventable. These preventable errors are often specific to the clinical unit where they occur; thus, the training to avoid them should reflect their unique context. We propose that multidisciplinary, in situ simulations, where clinical teams deliberately practice addressing these errors, are a feasible and acceptable educational approach to enhance patient safety. OBJECTIVES: To design a clinical unit-specific program of multidisciplinary, in situ simulations informed by recurrent patient safety incidents. To pilot and evaluate the participants’ perceptions of the effectiveness of this program as a means to enhance patient safety in their clinical environment. DESIGN/METHODS: This study was conducted on an inpatient pediatric ward at a pediatric tertiary care center. The ward’s Incident and Accident Reports from April 1, 2014 to March 31, 2015 were reviewed, and the most frequent and/or severe events that were amenable to simulation training were identified. Four simulation activities were created by a senior pediatric resident, and reviewed by the unit’s nurse educator and two hospitalist pediatricians. Participants included nurses, nursing assistants, pharmacists, medical students, and residents. Each activity consisted of a 10-minute simulation, followed by a 10-minute debriefing period. The debriefing helped the team identify the patient safety incident at play and discuss possible contributing factors. This was followed by specific feedback on the team’s communication. Participants were then emailed an anonymous survey, which was developed based on previously validated “quality-improvement” questionnaires. Questions gathered demographic information and probed for participants’ perceptions of this teaching method’s potential to impact patient safety. RESULTS: A total of 329 incident reports were reviewed. Four main themes were identified, including: Selection of IV solution, Feeding rate or type, Medication transcription and IV infiltration. These accounted for 4%(n=13), 7.6%(n=25), 7%(n=23), and 5%(n=16) of all reports, respectively. Eight simulations were performed, with 146 potential participants. Of these, 125 (86%) attended the simulations and 81 (65%) completed the survey. An average of 86% of participants answered “Strongly Agree/Positive” or “Agree/Positive” on questions about use of this approach to potentially enhance patient safety. CONCLUSION: Multidisciplinary, team-based in situ simulations are a feasible method to address recurrent patient safety incidents on a clinical unit. Participants felt that this educational approach was acceptable and effective. Further studies are needed to determine the impact of this program on the prevention of specific patient safety incidents.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.411
Teacher spread0.369 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes1
Has abstractyes

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