Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility

Abstract

Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support patient recruitment in the framework of a clinical trial for colorectal cancer screening. The utilisation of archetypes not only has proved satisfactory to achieve interoperability between CDSSs and EHRs but also offers various advantages, in particular from a data model perspective. First, the VHR/data models we work with are of a high level of abstraction and can incorporate semantic descriptions. Second, archetypes can potentially deal with different EHR architectures, due to their deliberate independence of the reference model. Third, the archetype instances we obtain are valid instances of the underlying reference model, which would enable e.g. feeding back the EHR with data derived by abstraction mechanisms. Lastly, the medical and technical validity of archetype models would be assured, since in principle clinicians should be the main actors in their development.

DDO: a diabetes mellitus diagnosis ontology

“…For example, we have previously proposed a unified data model based on the HL7 RIM v3 information model (Shaker et al 2015). Moreover, Marcos et al (2013) proposed a methodology for modeling EHR by using archetypes.…”

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

“…Electronic health records (EHRs) describing treatments and patient outcomes are rich but under-utilised. Mining local information included in EHR data-aware houses has already proved an effective way of managing a wide range of healthcare challenges such as supporting disease management system [66,67], pharmacovigilance [68], building models for predicting health risk assessment [69,70], communicating survival rates [71,72], making therapeutic recommendations [71,73], discovering co-morbidities and building support systems for clinical trial recruitment [74]. When longitudinal health data are sampled in a continuous fashion, meaningful and rich time-series can be collected in order to enable temporal data mining.…”

SemLinker: automating big data integration for casual users

“…The user formulates queries by utilizing the terms (concepts) defined by the ontology, and the queries are executed according to mappings between the ontological terms and their corresponding representations in the local schemas. Many current state-of-the-art ontology-based data integration systems follow Lenzerini's framework [19] to integrate structured and/or semi-structured data collected from heterogeneous data sources, such as [7,[21][22][23]. Although these systems can deliver effective and efficient data integration performance in many use cases, they typically require continuous human intervention to supervise the process of discovering mappings between the global ontology and the local schemas [1,14,24], which is a laborious and time-consuming task itself that requires schema matching expertise.…”

“…If |A| = 1 and |P| > 1 (line 18), an operation called Decomposition is performed on the attribute a stored in A before establishing mappings (lines [19][20][21][22][23][24][25][26][27][28]. After the operation, P is skipped from g using the auxiliary function Skip() for optimization purposes (line 29).…”

“…In the operation, a y is modeled as the parent node of the new virtual attributes (lines [23][24][25]. Similar to composition, the algorithm establishes two types of mappings to activate the decomposition transformation.…”