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Learning to diagnose collaboratively: validating a simulation for medical students.

ABSTRACT: Objectives: Physicians with different professional backgrounds often diagnose a patients' problem collaboratively. In this article, we first introduce a process model for collaborative diagnosing (CDR model), describe the development of a simulation used to empirically examine the facilitation of collaborative diagnostic reasoning. Based on a contemporary validity framework [1], we further suggest indicators for validity and collect initial evidence with respect to the scoring, generalization, extrapolation, and implication inferences to assess the validity of the simulation when used to assess effects of learning interventions. Method: In a quasi-experimental study, we assessed objectivity and reliability of the simulation and compared medical students with low and advanced prior knowledge to practitioners with high prior knowledge with respect to their diagnostic accuracy, diagnostic efficiency, information sharing skills, and their intrinsic cognitive load. Additionally, we obtained authenticity ratings from practitioners with high prior knowledge. Results: The results yielded satisfying initial evidence for the validity of the scoring and the extrapolation inferences as ratings are objective, and the simulation and the collaborative process is perceived as rather authentic. Additionally, participants on different levels of prior knowledge differ with respect to their diagnostic accuracy, diagnostic efficiency, information sharing skills, and their reported intrinsic cognitive load. With one exception (information sharing skills), the generalization inference seems to be valid as well. Conclusions: We conclude that collecting validity evidence for the simulation was an important step towards a better interpretation of the simulation. We found that the simulation is an authentic and valid representation of the chosen collaborative situation and that the collected validity evidence offers sufficient evidence for an initial validation of the simulation. Nevertheless, the validation process highlighted some important gaps that need further consideration. We further conclude that applying a validation model to the context of empirical research is promising and encourage other researchers to follow the example.

SUBMITTER: Radkowitsch A

PROVIDER: S-EPMC7499460 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Learning to diagnose collaboratively: validating a simulation for medical students.

Radkowitsch Anika A Fischer Martin R MR Schmidmaier Ralf R Fischer Frank F

GMS journal for medical education 20200915 5

<b>Objectives:</b> Physicians with different professional backgrounds often diagnose a patients' problem collaboratively. In this article, we first introduce a process model for collaborative diagnosing (CDR model), describe the development of a simulation used to empirically examine the facilitation of collaborative diagnostic reasoning. Based on a contemporary validity framework [1], we further suggest indicators for validity and collect initial evidence with respect to the scoring, generaliza ...[more]

PMID: 32984510

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Project description:BACKGROUND:Experiential learning plays a critical role in learner development. Kolb's 4-part experiential learning model consists of concrete experience, reflective observation, abstract conceptualization, and active experimentation in a recurring cycle. Most clinical environments provide opportunities for experiences and active experimentation but rarely offer structured means for reflection and abstract conceptualization that are crucial for learners to learn through experience. We created Learning Moment, a novel Web-based educational tool that integrates principles of asynchronous learning and learning portfolios to fulfill the reflection and abstract conceptualization aspects of Kolb's learning cycle in the modern clinical learning environment. Medical students log concise clinical "pearls" in the form of "learning moments" for reflection, review, and sharing with peers in a community of practice. OBJECTIVE:We sought to evaluate learners' experiences with Learning Moment via a qualitative study. METHODS:We employed purposive sampling to recruit medical students who used Learning Moment during their rotation. We conducted 13 semistructured interviews (10 individual interviews and one 3-person group interview) between January and March 2017 using an ethnographic approach and utilized a general inductive method to analyze and code for potential themes. RESULTS:A total of 13 students (five in their third year of medical school and eight in their fourth year) voluntarily participated in our qualitative interviews. Five of the 13 (38%) students intended to pursue emergency medicine as their chosen field of specialty. The median number of "learning moments" logged by these students is 6. From our analysis, three key themes emerged relating to the perceived impact of Learning Moment on student learning: (1) logging "learning moments" enhanced memorization, (2) improved learning through reflection, and (3) sharing of knowledge and experiences in a community of practice. CONCLUSIONS:Learning Moment was successfully implemented into the educational infrastructure in our department. Students identified three mechanisms by which the application optimizes experiential learning, including enabling the logging of "learning moments" to promote memorization, encouraging reflection to facilitate learning, and fostering the sharing of knowledge and experiences within a community of practice. The Learning Moment concept is potentially scalable to other departments, disciplines, and institutions as we seek to optimize experiential learning ecosystems for all trainees.

Dataset Information

Learning to diagnose collaboratively: validating a simulation for medical students.

Publications

Learning to diagnose collaboratively: validating a simulation for medical students.

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