# META-GLARE: A Computer-Interpretable Guideline System Shell
### The Importance of Fast Design and Prototyping of CIG Systems  
Notably, especially in the research field, an approach supporting fast design and prototyping when changing a CIG formalism, or proposing a new CIG formalism, can provide crucial advantages, at least in three different types of situations.  
First, when approaching new domains or phenomena, the formalism (and consequently the acquisition and execution tools) may need to be extended. Indeed, the history of many CIG systems in the literature shows the need to evolve from the initial version, extend the initial formalism, and/or overcome some of its limitations. For instance, the first version of GLIF (GuideLine Interchange Format) was proposed by a consortium involving Columbia University, Harvard University, and Stanford University in 1998 [10]. A second version, called GLIF2, appeared in 1999 [11], enriching GLIF with an execution engine. To overcome some of the limitations of the expressiveness of GLIF2 formalism, including the lack of a specification for the logical expressions, and the limited set of decision models, a third version (GLIF3) was proposed in 2004 [12]. Similarly, Asbru has been continuously developed, providing different versions (the last one we are aware of is version 7.3) [13]. The same has happened with GLARE. In the original version [14], GLARE supported only \"score-based\" decisions, while \"Boolean\" decisions were only introduced at a later stage. More recently, when facing guidelines about _alcohol-related disorders_, we have needed to tackle the issue that different healthcare agents need to be involved, so that the description of each action had to be enriched with different attributes, to model, e.g., the role and qualification required to be the agent in charge of such an action [15]. Recently [16], we have proposed further constructs, such as the possibility of representing complex action synchronizations (e.g., n actions will start when m of their predecessors' end) taken from workflow patterns [17].  
Second, to improve the acceptance and the use of CIG systems (and, more generally, computer decision support systems) by physicians, goals like formalism _suitability_ and _personalization_ are of primary importance. When acquiring a new CIG, _suitability_, intended as the _\"intuitive notion of expressiveness which takes the modelling effort into account\"_[18], is of primary importance. Notably, there is a _trade-off_ between the expressiveness of a formalism and its suitability: if the language is not expressive enough, the domain experts cannot model the desired phenomena (or have to devolve a lot of efforts to find \"tricky ways\" to model them), but if it is too rich, they have to consider and select among many different (and mostly not relevant for modeling the specific phenomena) constructs, thus devolving a lot of unnecessary efforts [19].  
A very related issue is (formalism) _personalization_: physicians really appreciate the possibility of \"representing phenomena as they want\", so the possibility of easily prototyping new systems based on \"personalized\" formalism provides crucial advantages in the CIG context.  
Last but not least, although the primary goal of most CIG systems (including GLARE) is _decision support_ for physicians, CIG can also be considered for other tasks (e.g., _education_--see Experiment 3 in Section 6; a posteriori cost/quality _evaluation_ of patient treatment performed at the organization level, etc.), requiring the representation of different aspects (and for which part of the usual description of CIGs may be irrelevant). In addition, in such cases, the possibility of easily moving to a different formalism can provide crucial advantages.