A Theoretical Framework for Fostering Diagnostic Competences with Simulations in Higher Education

Chernikova, Olga; Heitzmann, Nicole; Opitz, Ansgar; Seidel, Tina; Fischer, Frank

doi:10.1007/978-3-030-89147-3_2

Cited by 9 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, competence in diagnostic reasoning, whether it is done individually or collaboratively, also includes justifying the diagnosis and reaching it effectively. This is why, in addition to diagnostic accuracy, diagnostic justification and diagnostic efficiency should also be considered as secondary outcomes of the diagnostic reasoning process (Chernikova et al, 2022;Daniel et al, 2019). Diagnostic justification makes the reasoning behind the decision transparent and understandable for others (Bauer et al, 2022).…”

Section: Diagnostic Outcome: Accuracy Justification and Efficiencymentioning

confidence: 99%

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

Brandl,

Stadler,

Richters

et al. 2024

Intern. J. Comput.-Support. Collab. Learn

Self Cite

View full text Add to dashboard Cite

Collaborative skills are crucial in knowledge-rich domains, such as medical diagnosing. The Collaborative Diagnostic Reasoning (CDR) model emphasizes the importance of high-quality collaborative diagnostic activities (CDAs; e.g., evidence elicitation and sharing), influenced by content and collaboration knowledge as well as more general social skills, to achieve accurate, justified, and efficient diagnostic outcomes (Radkowitsch et al., 2022). However, it has not yet been empirically tested, and the relationships between individual characteristics, CDAs, and diagnostic outcomes remain largely unexplored. The aim of this study was to test the CDR model by analyzing data from three studies in a simulation-based environment and to better understand the construct and the processes involved (N = 504 intermediate medical students) using a structural equation model including indirect effects. We found various stable relationships between individual characteristics and CDAs, and between CDAs and diagnostic outcome, highlighting the multidimensional nature of CDR. While both content and collaboration knowledge were important for CDAs, none of the individual characteristics directly related to diagnostic outcome. The study suggests that CDAs are important factors in achieving successful diagnoses in collaborative contexts, particularly in simulation-based settings. CDAs are influenced by content and collaboration knowledge, highlighting the importance of understanding collaboration partners’ knowledge. We propose revising the CDR model by assigning higher priority to collaboration knowledge compared with social skills, and dividing the CDAs into information elicitation and sharing, with sharing being more transactive. Training should focus on the development of CDAs to improve CDR skills.

show abstract

Section: Diagnostic Outcome: Accuracy Justification and Efficiencymentioning

confidence: 99%

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

Brandl,

Stadler,

Richters

et al. 2024

Intern. J. Comput.-Support. Collab. Learn

Self Cite

View full text Add to dashboard Cite

show abstract

Diagnosing Collaboratively: A Theoretical Model and a Simulation-Based Learning Environment

Radkowitsch

Sailer

Fischer³

et al. 2022

Learning to Diagnose With Simulations

Self Cite

View full text Add to dashboard Cite

In their daily practice, physicians with different professional backgrounds often diagnose a patient’s problem collaboratively. In those situations, physicians not only need to be able to diagnose individually, but also need additional collaborative competences such as information sharing and negotiation skills (Liu et al., 2015), which can influence the quality of the diagnostic outcome (Tschan et al., 2009). We introduce the CDR model, a process model for collaborative diagnostic reasoning processes by diagnosticians with different knowledge backgrounds. Building on this model, we develop a simulation in order to assess and facilitate collaborative diagnostic competences among advanced medical students. In the document-based simulation, learners sequentially diagnose five patients by inspecting a health record for symptoms. Then, learners request a radiological diagnostic procedure from a simulated radiologist. By interacting with the simulated radiologist, the learners elicit more evidence for their hypotheses. Finally, learners are asked to integrate all information and suggest a final diagnosis.

show abstract

Conclusions and Outlook: Toward more Systematic Research on the Use of Simulations in Higher Education

Opitz

Fischer²,

Seidel

et al. 2022

Learning to Diagnose With Simulations

Self Cite

View full text Add to dashboard Cite

This final chapter starts with an overview of the simulations presented throughout the book. All of the simulations are tools to test and foster diagnostic competences. The simulations differ regarding diagnostic modes, sources of information, domains, and topics. The chapter then revisits the four overarching research questions from the introduction and lies out potential avenues for future research: (1) Diagnostic activities are a promising concept to explore the processes that are central for generating learning outcomes. (2) Optimal means of supporting learners in simulations might include the presentation of information, prompts, reflection phases, and role-taking. (3) Various characteristics of the learners, such as their expertise level, as well as the simulation, such as its authenticity, could mediate the effects of instructional support. (4) If simulations are adaptable, between learners or within the same simulation, they might increase the learning potential even further. The chapter then gives examples for how the projects presented in this book will address these four topics. The outlook ends with a call for interdisciplinary research.

show abstract

A Theoretical Framework for Fostering Diagnostic Competences with Simulations in Higher Education

Cited by 9 publications

References 40 publications

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

Diagnosing Collaboratively: A Theoretical Model and a Simulation-Based Learning Environment

Conclusions and Outlook: Toward more Systematic Research on the Use of Simulations in Higher Education

Contact Info

Product

Resources

About