Differential Diagnostics

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Kite Award 2022
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Learning with
digital tools

The one-​week course Differential Diagnostics is an integral course in the trajectory of the medical studies at ETHZ and takes place at the very end of the third study year (Bachelor’s degree). With the course we aim to provide a broad and an as complete as possible overview in the differential diagnostics of diseases in all organ systems to students. Originally, we planned the course to take place in hospitals across Germanspeaking Switzerland, where we intended to assess the students’ diagnostic skills. For this purpose, we designed various exercises for students in a clinical setting in order to simulate the complexity of later clinical requirements as realistic as possible. We expected students to interview, examine, and assess simulated and real patients. Furthermore, we intended students to draw correct differential diagnostic conclusions on the basis of patient files because this is important in the sense of preparing for patient encounters. Additionally, we planned case studies discussions in plenary.

Implementation of the course during the time of distance learning

However, due to the CoVid-​19 pandemic the course could not take place in the clinical setting as originally planned in spring semesters 2020 and 2021. Consequently, we had to implement adaptations very quickly, especially in 2020, with the goal of maintaining interactivity and practice time between students and some sort of patients. Fortunately, the medical faculty of ETHZ was planning to implement virtual patients in the medical curriculum already before the pandemic started. As the evaluation process of virtual learning platforms was nearly completed at that time, the I-​Human Patients platform was selected. I-​Human Patients simulates a complete medical patient encounter with animated avatars, human physiology and pathophysiology, and 3D anatomy for the purpose of improving users’ patient assessment and clinical reasoning skills, and patient outcomes. Furthermore, learners receive online guidance, feedback, and coaching at every step of the learning process if needed and desired.

Finally, the course took place completely online where students were solving patient problems from home in I-​Human Patients during the whole course week. We combined interactive patient problemsolving with live lectures and patient case discussions. The procedure was similar for each day. This means that every evening we sent detailed instructions to students via e-​mail about the proceeding of the next day. Inter alia, this included information about the patient scenarios to be individually solved the next morning. Usually, these were two to three scenarios, each taking about 45 – 60 minutes. In the afternoon, the whole class met on Zoom for a 90-​minutes consolidation lecture. During this time, the main lecturer, Christian Schmied, discussed the patient cases which were solved in the morning, pointed out important issues, and provided students with general information about the corresponding topic. Additionally, students could freely ask questions. We gradually increased the difficulty level of the patient cases from day to day. This also included varied provision of automated feedback. In addition to these tasks, students had to solve multiple-​choice quizzes at the end of each course day which were related to the daily patient cases.

Due to the pandemic issues, the course passing type was reclassified to an ungraded semester performance. Hence, students passed the course if they completed all assigned tasks, which included to work through all patient cases, to attend all online lectures, and to solve all quizzes.

Before and during the course week, students could also ask the assisting lecturer, Christian Fässler, for technical support in case something did not work properly with the I-​Human Patients platform. At the end of the course, the students completed a questionnaire in which they were asked about their general course experience but also more specifically about the application of and their satisfaction with IHuman Patients and virtual patient scenarios.

From a learning science perspective, virtual patients represent aspects of situated learning. Several beneficial effects on learning and transfer have been attributed to situated learning. The chosen sequence of learning activities each day in the course of first working through patient problem and then attending a consolidation lecture is based on the so called problem-​solving prior to instruction design. It is suggested that problem-​solving preceding instruction results in better conceptual understanding and transfer outcomes than instruction-​first learning approaches for comparisons carried out in the domain of medicine.

Summarized, we could implement a convincing alternative to teach one of the core disciplinary practices in medicine which heavily relies on clinical experience – differential diagnostics. With virtual patients we could teach this cognitively very demanding process and clinical reasoning, where students could train on an appropriate level with being as situated as possible.

Overall concept of the course before the pandemic – during – after

The course Differential Diagnostics was first introduced in spring semester 2020. Hence, it never took place in the way it was originally planned.

Virtual, but also simulated and real patients represent aspects of situated learning. Situated learning describes a type of learning activity in which learning is placed in a particular context. An example related to the present project is to be (virtually) situated in a hospital or clinical practice, to take the role as a doctor and to deal with an actual patient problem. The beneficial effects of situated learning on learning and transfer might be evoked through various mechanisms, such as that

(a) knowledge and learning is grounded in one or multiple contexts,

(b) learning takes place through an interaction and transaction between people and their environments,

(c) abstract concepts are taught in a real-​world context,

(d) the value and relevance of learned knowledge to the learner amplifies, and

(e) situated learning activities tend to provoke emergent meta-​cognitive behaviors.

The problem-​solving prior to instruction instructional design features an initial problem-​solving phase which is followed by a direct instruction. In the problem solving phase, students try to generate own solution ideas to a novel (patient) problem as preparation for learning from subsequent instruction. For this, they need to activate relevant prior knowledge, become aware of knowledge gaps, and become curious. It is assumed that these three processes evoked during the problem-​solving phase prepare students for learning in the instruction phase. This is why this sequencing approach might result in better conceptual understanding and transfer outcomes than instruction-​first learning approaches.

Consequently, we will advocate the established concept of situated learning with real, simulated, and virtual patients after the pandemic. Furthermore, we will continue to follow the problem-​solving prior to instruction approach.

Course Description

This module gives a broad overview of the differential diagnostic of diseases and shall assess the diagnostic skills of the medical students. For this purpose, the students will practice different situations to simulate upcoming requirements in daily clinical practice as realistic as possible.
The students know the most important differential diagnostics of all organic systems.

The students know advantages and drawbacks of the most important diagnostic tools.

The students are able to perform an accurate clinical examination (incl. patient’s personal and systemic history, physical examination) and to name the adequate differential diagnostic.

The students are able to create a first diagnostic approach on the base of the patient’s health record, recognize lacking information and add this to complete their assessment.
Ungraded semester performance