Abstract
BackgroundShortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators.ObjectiveThe goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator.MethodsWe trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case.ResultsWe developed a VPS called Hepius that allows students to gather clinical information from the patient’s medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance.ConclusionsBy combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.
Highlights
Learning clinical diagnostic reasoning is a critical challenge for medical students, as fallacies in diagnostic reasoning may lead to patient mistreatment with negative consequences on patient health and health care costs [1]
We developed a virtual patient simulator (VPS) called Hepius that allows students to gather clinical information from the patient’s medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language
It is aimed at training experienced doctors in facing COVID-19 cases that evolve over time according to the user’s diagnostic and therapeutic interventions, which are selected from a predefined list of possibilities
Summary
Learning clinical diagnostic reasoning is a critical challenge for medical students, as fallacies in diagnostic reasoning may lead to patient mistreatment with negative consequences on patient health and health care costs [1]. This promotes the need for clinical training methods that do not require bedside didactic activities and that do not necessarily entail continuous direct supervision by experienced doctors [6,7] Examples of these methods are simulators, which were developed to support learning of specific medical procedures, such as laparoscopy [8], and to train students in clinical diagnostic reasoning as with virtual patient simulators (VPSs) [9]. A VPS is a computer program that simulates real-life clinical scenarios, enabling students to emulate the role of a doctor by obtaining a medical history, performing a physical exam, and making diagnostic and therapeutic decisions [10] These computer-based simulators may complement traditional training techniques without requiring direct ward attendance [11]. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
