## 6 Discussion and Conclusions
Thus, combining this idea with the augmented EHR approach may lead to a more accurate and efficient exploitation of EHR data for secondary uses, given that data from the original EHR and from external sources can be processed starting from the augmented EHR (or an abstraction thereof). In this work, EHR augmentations of potential interest were identified in the context of standard EHRs using archetypes and available Semantic Web resources. Starting from these augmentations, a method to obtain an implementation that serves for such augmentationswas designed and implemented. Although this method was designed in close connection with the EHR schema, the EHR augmentations can be modeled regardless of the EHR extract that will use them. Therefore, a varied set of EHR augmentations could potentially be provided for users, who would be able to choose the most convenient EHR augmentation to enrich specific EHR extracts with additional contents. Furthermore, augmentations were designed to be reused not only to enrich existing EHR archetypes, but also results from CDS systems. At present, most of the process of designing and implementing an EHR augmentation is manual. Existing Semantic Web tools to discover equivalent relationships between EHR coded values and terms from the LOD cloud were explored for this work. However, most schema definitions are not necessarily well formed in the domain of LOD [39] and cannot be seamlessly crawled. In addition, the quality of these LOD datasets in terms of completeness, consistency, conciseness, and interlinking remains a challenge [40]. VoID descriptors allow identification of public LOD datasets when not provided by the organization supporting the ontology. Nevertheless, not all of the published datasets provide VoID descriptors, and those providing VoID descriptors show the relevant information (e.g., the URL prefix of data items in the target dataset) located in different properties for each VoID descriptor. To promote the automatization of the augmented EHR design process, we proposed an algorithm to suggest where to place the slot for the augmentation archetype in the augmented EHR archetype. The algorithm is based on the syntactic structure of the initial EHR archetype, and considers semantic relationships by trying to identify a valid position near to the EOI (e.g., a sibling of the EOI, under the same parent component in the archetype). Although the proposed solution requires some manual interventions, it provides the advantage that at execution time it is completely automatic. Therefore, once an augmented EHR is defined, specific patient EHR extracts can be automatically augmented without restrictions or manual interventions. The presented method was designed aiming to be as automatic as possible, considering the current limitations of RDF schemas and dataset descriptions in LOD. The main aim of the process requiring manual intervention is the identification of potentially relevant augmentation content. This is a portion of linked data from the Semantic Web whose potential relevance must be established by clinical experts. However, there are tools that can assist in the process of designing augmented EHRs; for example, linked data crawlers such as LDSpider [41] could be used to discover potentially interesting augmentation contents starting from the EOI. In addition, visual SPARQL query editors such as Gruff [42] may assist in the exploration of the augmentation content and can be helpful to build the corresponding SPARQL queries. Artificial intelligence methods can help to automatize this process and reduce manual interventions, but we consider this would be part of a different work.