# META-GLARE: A Computer-Interpretable Guideline System Shell
## 6 Applying Meta-GLARE
In our opinion, this experiment is important to highlight that META-GLARE supports designers in defining a new CIG system (considering both the acquisition and the execution tools) with minimal effort. Indeed, in this specific experiment, no programming at all was needed. However, this was because all the attribute types used in GLARE were already present in the META-GLARE libraries. **Experiment 3. ** META-GLARE for education. \"Traditional\" courses and books aim at teaching a large body of knowledge to students, e.g., anatomy, disease description and treatment, etc. On the other hand, \"operational\" knowledge concerning how to operate (diagnose and treat) specific patients is usually neglected in medical texts, and is learned by students only \"_by practicing\"_[34]. In recent years, several approaches have been proposed to take advantage of computer science to facilitate the teaching of such \"operational\" aspects,  
Figure 3: A screenshot of the graphical interface of META-GLARE DEFINITION_EDITOR. The screenshot shows a snapshot of the acquisition of the “dataEnquiry” control attribute. Slots (e.g., Name) and pop-up windows are used in order to acquire the different properties of the attribute. In particular, the tree at the bottom of the figure highlights the acquisition of the control method for the “standard” task of “Enquiry”. The proper Java method is selected from the library through the pop-up window shown in the right part of the figure. with a specific emphasis on simulation [34]. However, until now, no approach has tried to exploit the potentialities of CIG systems for education. Indeed, given the data of a real patient or of an invented one, a CIG system like GLARE can be exploited to simulate the execution of any acquired CIG on the patient, to train medical students about how to operate on specific patients. We have started to pursue such a research line in the ROPHS (Report on the Piedmont Health System) project. In such a project, in cooperation with the physicians in the project, we first used GLARE to acquire a guideline for polytrauma. In the second step, the expert physicians have defined a set of \"typical\" polytrauma patients. Finally, GLARE has been used in a medical course to show students how the polytrauma guideline recommends operating on such patients [33]. The experience has been quite positive and has highlighted the relevance of addressing new educational issues, like the use of a CIG system for testing students. For testing, given a patient and a CIG, at each step in the diagnosis and treatment of the patient, (i) the student should first specify how she would operate on the patient, (ii) then the CIG system should compare the student's choice to the CIG's recommendation, and (iii) propose the student's discrepancies (if any). In more detail, testing can focus on three main aspects: (1) the student's ability to identify (all and only) the patient's data needed in order to take the decisions regarding the patient, and their ability to make the correct (2) decisions regarding the patient at hand. To support tasks (i)-(iii), a substantially new (compared to \"traditional\" systems in the literature) CIG system has to be developed. However, we are taking advantage of META-GLARE to implement it, and this choice is greatly facilitating the achievement of our goal, as sketched below. The implementation of the new system that we have called GLARE-Edu is currently ongoing, following the highlights discussed in [35; 36]). Our implementation is organized in two parts. **Part 1. ** In the first (ongoing) phase, we have started from the consideration that, in general, no modification of the chosen CIG formalism is needed when moving from the \"standard\" task (i.e., support to physicians) to testing for education. As a consequence, we have chosen to maintain GLARE's formalism and to start our implementation of GLARE-Edu on top of the re-implementation of GLARE we have discussed in Experiment 1.