Automatic feedback generation in virtual patients using semantic web technologies

A variety of computer systems called virtual patients are available in medical education today. Virtual patients are designed to emulate realistic clinical cases on a computer, and help students to practice diagnosis and clinical reasoning. They are used as an integral part of the curriculum in many medical schools. However, the technologies currently used to build virtual patients present limitations. Feedback has to be edited manually by medical experts, and the feedback provided is often not adapted to each student's interactions with the virtual patient. This makes creating and editing a virtual patient time-consuming, and limits its pedagogical impact. Indeed, relevant feedback is crucial to help students assess and reflect on their performance, reflect on their decisions and improve their clinical reasoning skills. This paper presents research on automatic feedback generation for virtual patients, using semantic web technologies. To generate feedback, a computer model has been designed to represent virtual patients and students’ interactions, using semantic web technologies. The use of semantic web technologies allows a computer readable connection between medical conditions, their symptoms and the corresponding examinations. Some of these connections can be pulled from existing linked data available on the web, which would facilitate the creation and maintenance of virtual patient data. A survey has been conducted to determine the most useful types of feedback for medical students. Relating this encoded knowledge to data describing the student’s choices of examinations allows the automatic generation of such feedback in virtual patients

Modelling virtual patients and generating feedback using semantic web technologies

A variety of computer programs called virtual patients systems are available today. Virtual patients are designed to emulate realistic clinical cases on a computer, and help students to practice diagnosis and clinical reasoning. They are used as an integral part of the curriculum in many medical schools. However, the technologies currently used to build virtual patients present limitations. Feedback has to be edited manually by medical experts, and the feedback provided is often not adapted to each student's interactions with the virtual patient. This makes creating and editing a virtual patient time-consuming, and limits its pedagogical impact. This presentation demonstrates research on automatic feedback generation for virtual patients, using a group of methods and technologies collectively known as the semantic web. The semantic web is designed to formally represent information about digital documents and other resources (such as people and events) using RDF (Resources Description Framework). It is also possible to describe concepts, classify them and define their properties using OWL (Web Ontology Language). These formal languages also allow re-use of data from external sources from the web. To generate feedback, an adequate computer model has to be designed to represent virtual patients and students’ interactions. The semantic web allows rich data modelling, and is therefore superior to traditional data technologies such as relational databases and XML for this purpose

Locomotor Virtual Patient Scene

BM1 Locomotor Virtual Patient screensho

Year 1 Virtual Patient Screen Capture file

Screen capture used for MedB & VP conference 201

Virtual patients: year 1

At the University of Southampton Medical School virtual patients are the key to delivering its patient centred curriculum. In year 1, the aim of the virtual patients is to present a realistic clinical scenario from which students can experience a patient journey and the clinical processes involved. The virtual patients consist of interactive linear animated clinical scenarios with interactive tasks and embedded guided learning materials. They are designed to guide year 1 students through each clinical process whilst helping them apply and integrate their knowledge of the basic sciences in a clinical context