Enhancing Nurse and Physician Collaboration in Clinical Decision Making Through High-fidelity Interdisciplinary Simulation Training

BackgroundHigh-Fidelity Simulation (HFS) has great potential to improve decision-making in clinical practice. Previous studies have found HFS promotes self-confidence, but its effectiveness in clinical practice has not been established. The aim of this research is to establish if HFS facilitates learning that informs decision-making skills in clinical practice using MultipleCriteria DecisionMaking Theory (MCDMT).MethodsThe sample was 2nd year undergraduate pre-registration adult nursing students.MCDMT was used to measure the students’ experience of HFS and how it developed their clinical decision-making skills. MCDMT requires characteristic measurements which for the learning experience were based on five factors that underpin successful learning, and for clinical decision-making, an analytical framework was used. The study used a repeated-measures design to take two measurements: the first one after the first simulation experience and the second one after clinical placement. Baseline measurements were obtained from academics. Data were analysed using the MCDMT tool.ResultsAfter their initial exposure to simulation learning, students reported that HFS provides a high-quality learning experience (87%) and supports all aspects of clinical decision-making (85%). Following clinical practice, the level of support for clinical decision-making remained at 85%, suggesting that students believe HFS promotes transferability of knowledge to the practice setting.ConclusionOverall, students report a high level of support for learning and developing clinical decision-making skills from HFS. However, there are no comparative data available from classroom teaching of similar content so it cannot be established if these results are due to HFS alone.

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Evaluation of Heart Rate Assessment Timing, Communication, Accuracy, and Clinical Decision-Making during High Fidelity Simulation of Neonatal Resuscitation

International Journal of Pediatrics ◽

10.1155/2014/927430 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5 ◽

Cited By ~ 6

Author(s):

Win Boon ◽

Jennifer McAllister ◽

Mohammad A. Attar ◽

Rachel L. Chapman ◽

Patricia B. Mullan ◽

...

Keyword(s):

Decision Making ◽

Heart Rate ◽

Neonatal Intensive Care ◽

Clinical Decision Making ◽

Neonatal Resuscitation ◽

Clinical Decision ◽

High Fidelity ◽

High Fidelity Simulation ◽

Chi Square ◽

Chi Square Analysis

Objective. Accurate heart rate (HR) determination during neonatal resuscitation (NR) informs subsequent NR actions. This study’s objective was to evaluate HR determination timeliness, communication, and accuracy during high fidelity NR simulations that house officers completed during neonatal intensive care unit (NICU) rotations.Methods. In 2010, house officers in NICU rotations completed high fidelity NR simulation. We reviewed 80 house officers’ videotaped performance on their initial high fidelity simulation session, prior to training and performance debriefing. We calculated the proportion of cases congruent with NR guidelines, using chi square analysis to evaluate performance across HR ranges relevant to NR decision-making: <60, 60–99, and ≥100 beats per minute (bpm).Results. 87% used umbilical cord palpation, 57% initiated HR assessment within 30 seconds, 70% were accurate, and 74% were communicated appropriately. HR determination accuracy varied significantly across HR ranges, with 87%, 57%, and 68% for HR <60, 60–99, and ≥100 bpm, respectively (P<0.001).Conclusions. Timeliness, communication, and accuracy of house officers’ HR determination are suboptimal, particularly for HR 60–100 bpm, which might lead to inappropriate decision-making and NR care. Training implications include emphasizing more accurate HR determination methods, better communication, and improved HR interpretation during NR.

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Use of EHR-based simulation to diagnose aetiology of information gathering issues in struggling learners: a proof of concept study

BMJ Simulation and Technology Enhanced Learning ◽

10.1136/bmjstel-2017-000217 ◽

2017 ◽

Vol 4 (2) ◽

pp. 92-94 ◽

Cited By ~ 1

Author(s):

Vishnu Mohan ◽

Gretchen Scholl ◽

Jeffrey A Gold

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Large Fraction ◽

Clinical Information ◽

Clinical Decision ◽

Information Gathering ◽

High Fidelity ◽

Struggling Learners ◽

Effective Decision Making ◽

Main Component

Learners who struggle with clinical decision making are often the most challenging to identify and remediate. While for some learners, struggles can be directly traced to a poor knowledge base, for many others, it is more difficult to understand the reason for their struggles. One of the main component of effective decision making is access to accurate and complete clinical information. The electronic health record (EHR) is the main source of clinical information and, with its widespread adoption, has come increased realisation that a large fraction of users have difficulty in effectively gathering and subsequently processing information out of the EHR. We previously documented that high-fidelity EHR-based simulation improves EHR usability and, when combined with eye and screen tracking, generates important measures of usability. We hypothesised that the same simulation exercise could help distinguish whether learners had difficulty in knowledge, information gathering or information processing. We report the results of the first three struggling learners who participated in this exercise. In each case, the simulation was able to ‘diagnose’ the aetiology for the learners’ struggle and assist in formulating an appropriate solution. We suggest that high-fidelity EHR-based simulation can be a powerful tool in the standard approach to understanding struggling learners.

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