## 6 Discussion and Conclusions  
This work introduces the augmented EHR concept as the enrichment of an existing EHR with additional contents (i.e., information and/or knowledge) derived from different external information sources. Furthermore, a preliminary approach to build the augmented EHR using Semantic Web sources is proposed by enriching EHRs with patient-related data (at the instance level) and/or data related to medical concepts (at the information model level). Additionally, practical foundations to express Semantic Web data as normalized EHR extracts are defined. This solution provides an alternative means to bridge the gap between healthcare IT standards and the Semantic Web, by transforming Semantic Web contents into EHR extracts. Moreover, note that the approach can be extended to enrich the EHR with additional contents derived from external sources other than those from the Semantic Web, by designing intermediate transformations to build a semantic representation of such sources, and then applying this method taking such a semantic representation as an external source. Several previous works have addressed combining the EHR with other information sources [11; 12; 13; 14; 16; 30; 31; 32], most of which have focused on converting EHR data to Semantic Web formats. These approaches aim at reasoning with the data, and thus infer new facts from the patient's EHR. However, Semantic Web formats are not natively supported by EHR systems. The presented work introduces an approach in the opposite direction, aiming to bring additional contents from Semantic Web sources to the EHR of a given patient, in such a way that normalized healthcare IT systems and services can leverage these contents. Other works address methods to incorporate additional potentially relevant information into the EHR [17; 18; 19; 33]. Nevertheless, this information (i.e., the augmentation content) is not normalized according to an EHR standard, leading to a limited degree of interoperability, in contrast to the augmented EHR approach described in this paper, in which the content from external sources is normalized and integrated as a part of the EHR. For example, this allows processing of the augmented EHR in subsequent transformations as a normalized EHR, processing both the augmentation content and the patient EHR data as the same entity (i.e., the EHR), which may be especially useful when feeding the EHR with data from other information systems, such as third-party EHR systems, clinical decision support systems (CDS), or computerized clinical guidelines (CCG). Special mention is necessary of Infobuttons [24], incorporated as part of the HL7 standard (HL7's Context-Aware Knowledge Retrieval), which is able to retrieve external information from the point of care (i.e., EHR or PHR). However, our work improves such an approach in two ways. First, our methodology allows retrieving relevant information from the Semantic Web, which offers significant advantages in contrast with other kinds of information sources, such as reasoning over data and dealing with heterogeneous data sources [34]. The Infobuttons approach does not specify how to integrate contents from the Semantic Web into the EHR. In addition, our solution establishes a method to retrieve additional relevant information as EHR extracts compliant with EHR archetypes, enhancing interoperability with other EHR systems compliant with EHR archetype-based standards. This is in contrast to the Infobuttons approach, which does not state methods to retrieve the additional data in compliance with EHR archetypes, limiting the interoperability of the retrieved information with other EHR systems compliant with EHR archetype-based standards. Works dealing with EHR data and CDS integration highlight the lack of a standard API to access EHR data [35; 36]. Despite the current lack of a standard API, the inputs of CDS systems, and an approach to extract these inputs from the EHR data, can be defined through standard EHR models [12; 13; 37; 38].