Archetype Modeling Methodology

[EN] Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.This work was partially funded by grant DI-14-06564 (Doctorados Industriales) of the Ministerio de Economia y Competitividad of Spain. The authors would also thank the participants of all R&D projects that have served to evaluate and improve the presented methodology.Moner Cano, D.; Maldonado Segura, JA.; Robles Viejo, M. (2018). Archetype Modeling Methodology. Journal of Biomedical Informatics. 79:71-81. https://doi.org/10.1016/j.jbi.2018.02.003S71817

An Investigation of Semantic Links to Archetypes in an External Clinical Terminology through the Construction of Terminological Shadows

The two-level model based specifications for electronic health record communication EHRcom (ISO 13606) and openEHR both support the embedding of terminological references in Archetypes. This terminological binding can be created manually by a health terminology expert during Archetype design, and the binding is assessed during Archetype evaluation. There has also been some recent work on using lexical queries to generate term sets to represent concepts in Archetypes. This work created an information construct which we call a Terminological Shadow that links Archetype nodes to sets of candidate concepts from a terminology system. The coding scheme used for this work is SNOMED-CT. The proposed Shadows can be used to facilitate the mapping between an Archetype information model and terminological systems. A framework, which also acts as an analysis tool, has been created to construct Shadows from Archetypes. The work also demonstrates how the framework can be used to evaluate different searching algorithms by comparing the search results to the existing bound SNOMED codes

The CAP cancer protocols – a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

BACKGROUND: The Cancer Biomedical Informatics Grid (caBIG™) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available. METHODS: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model – an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG™ models. RESULTS: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems. CONCLUSION: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG™ semantic modeling methodology

Performance Analysis and Assessment of a TF-IDF Based Archetype-SNOMED-CT Binding Algorithm

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors\u27 automatic search and used for assessing the algorithm\u27s performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process

Combining ontologies and rules with clinical archetypes

Al igual que otros campos que dependen en gran medida de las funcionalidades ofrecidas por las tecnologías de la información y las comunicaciones (IT), la biomedicina y la salud necesitan cada vez más la implantación de normas y mecanismos ampliamente aceptados para el intercambio de datos, información y conocimiento. Dicha necesidad de compatibilidad e interoperabilidad va más allá de las cuestiones sintácticas y estructurales, pues la interoperabilidad semántica es también requerida. La interoperabilidad a nivel semántico es esencial para el soporte computarizado de alertas, flujos de trabajo y de la medicina basada en evidencia cuando contamos con la presencia de sistemas heterogéneos de Historia Clínica Electrónica (EHR). El modelo de arquetipos clínicos respaldado por el estándar CEN/ISO EN13606 y la fundación openEHR ofrece un mecanismo para expresar las estructuras de datos clínicos de manera compartida e interoperable. El modelo ha ido ganando aceptación en los últimos años por su capacidad para definir conceptos clínicos basados en un Modelo de Referencia común. Dicha separación a dos capas permite conservar la heterogeneidad de las implementaciones de almacenamiento a bajo nivel, presentes en los diferentes sistemas de EHR. Sin embargo, los lenguajes de arquetipos no soportan la representación de reglas clínicas ni el mapeo a ontologías formales, ambos elementos fundamentales para alcanzar la interoperabilidad semántica completa pues permiten llevar a cabo el razonamiento y la inferencia a partir del conocimiento clínico existente. Paralelamente, es reconocido el hecho de que la World Wide Web presenta requisitos análogos a los descritos anteriormente, lo cual ha fomentado el desarrollo de la Web Semántica. El progreso alcanzado en este terreno, con respecto a la representación del conocimiento y al razonamiento sobre el mismo, es combinado en esta tesis con los modelos de EHR con el objetivo de mejorar el enfoque de los arquetipos clínicos y ofrecer funcionalidades que se corresponden con nivel más alto de interoperabilidad semántica. Concretamente, la investigación que se describe a continuación presenta y evalúa un enfoque para traducir automáticamente las definiciones expresadas en el lenguaje de definición de arquetipos de openEHR (ADL) a una representación formal basada en lenguajes de ontologías. El método se implementa en la plataforma ArchOnt, que también es descrita. A continuación se estudia la integración de dichas representaciones formales con reglas clínicas, ofreciéndose un enfoque para reutilizar el razonamiento con instancias concretas de datos clínicos. Es importante ver como el acto de compartir el conocimiento clínico expresado a través de reglas es coherente con la filosofía de intercambio abierto fomentada por los arquetipos, a la vez que se extiende la reutilización a proposiciones de conocimiento declarativo como las utilizadas en las guías de práctica clínica. De esta manera, la tesis describe una técnica de mapeo de arquetipos a ontologías, para luego asociar reglas clínicas a la representación resultante. La traducción automática también permite la conexión formal de los elementos especificados en los arquetipos con conceptos clínicos equivalentes provenientes de otras fuentes como son las terminologías clínicas. Dichos enlaces fomentan la reutilización del conocimiento clínico ya representado, así como el razonamiento y la navegación a través de distintas ontologías clínicas. Otra contribución significativa de la tesis es la aplicación del enfoque mencionado en dos proyectos de investigación y desarrollo clínico, llevados a cabo en combinación con hospitales universitarios de Madrid. En la explicación se incluyen ejemplos de las aplicaciones más representativas del enfoque como es el caso del desarrollo de sistemas de alertas orientados a mejorar la seguridad del paciente. No obstante, la traducción automática de arquetipos clínicos a lenguajes de ontologías constituye una base común para la implementación de una amplia gama de actividades semánticas, razonamiento y validación, evitándose así la necesidad de aplicar distintos enfoques ad-hoc directamente sobre los arquetipos para poder satisfacer las condiciones de cada contexto

Archetype development and governance methodologies for the electronic health record

[ES] La interoperabilidad semántica de la información sanitaria es un requisito imprescindible para la sostenibilidad de la atención sanitaria, y es fundamental para afrontar los nuevos retos sanitarios de un mundo globalizado. Esta tesis aporta nuevas metodologías para abordar algunos de los aspectos fundamentales de la interoperabilidad semántica, específicamente aquellos relacionados con la definición y gobernanza de modelos de información clínica expresados en forma de arquetipo. Las aportaciones de la tesis son: - Estudio de las metodologías de modelado existentes de componentes de interoperabilidad semántica que influirán en la definición de una metodología de modelado de arquetipos. - Análisis comparativo de los sistemas e iniciativas existentes para la gobernanza de modelos de información clínica. - Una propuesta de Metodología de Modelado de Arquetipos unificada que formalice las fases de desarrollo del arquetipo, los participantes requeridos y las buenas prácticas a seguir. - Identificación y definición de principios y características de gobernanza de arquetipos. - Diseño y desarrollo de herramientas que brinden soporte al modelado y la gobernanza de arquetipos. Las aportaciones de esta tesis se han puesto en práctica en múltiples proyectos y experiencias de desarrollo. Estas experiencias varían desde un proyecto local dentro de una sola organización que requirió la reutilización de datos clínicos basados en principios de interoperabilidad semántica, hasta el desarrollo de proyectos de historia clínica electrónica de alcance nacional.[CA] La interoperabilitat semàntica de la informació sanitària és un requisit imprescindible per a la sostenibilitat de l'atenció sanitària, i és fonamental per a afrontar els nous reptes sanitaris d'un món globalitzat. Aquesta tesi aporta noves metodologies per a abordar alguns dels aspectes fonamentals de la interoperabilitat semàntica, específicament aquells relacionats amb la definició i govern de models d'informació clínica expressats en forma d'arquetip. Les aportacions de la tesi són: - Estudi de les metodologies de modelatge existents de components d'interoperabilitat semàntica que influiran en la definició d'una metodologia de modelatge d'arquetips. - Anàlisi comparativa dels sistemes i iniciatives existents per al govern de models d'informació clínica. - Una proposta de Metodologia de Modelatge d'Arquetips unificada que formalitza les fases de desenvolupament de l'arquetip, els participants requerits i les bones pràctiques a seguir. - Identificació i definició de principis i característiques de govern d'arquetips. - Disseny i desenvolupament d'eines que brinden suport al modelatge i al govern d'arquetips. Les aportacions d'aquesta tesi s'han posat en pràctica en múltiples projectes i experiències de desenvolupament. Aquestes experiències varien des d'un projecte local dins d'una sola organització que va requerir la reutilització de dades clíniques basades en principis d'interoperabilitat semàntica, fins al desenvolupament de projectes d'història clínica electrònica d'abast nacional.[EN] Semantic interoperability of health information is an essential requirement for the sustainability of healthcare, and it is essential to face the new health challenges of a globalized world. This thesis provides new methodologies to tackle some of the fundamental aspects of semantic interoperability, specifically those aspects related to the definition and governance of clinical information models expressed in the form of archetypes. The contributions of the thesis are: - Study of existing modeling methodologies of semantic interoperability components that will influence in the definition of an archetype modeling methodology. - Comparative analysis of existing clinical information model governance systems and initiatives. - A proposal of a unified Archetype Modeling Methodology that formalizes the phases of archetype development, the required participants, and the good practices to be followed. - Identification and definition of archetype governance principles and characteristics. - Design and development of tools that provide support to archetype modeling and governance. The contributions of this thesis have been put into practice in multiple projects and development experiences. These experiences vary from a local project inside a single organization that required a reuse on clinical data based on semantic interoperability principles, to the development of national electronic health record projects.This thesis was partially funded by the Ministerio de Economía y Competitividad, ayudas para contratos para la formación de doctores en empresas “Doctorados Industriales”, grant DI-14-06564 and by the Agencia Valenciana de la Innovación, ayudas del Programa de Promoción del Talento – Doctorados empresariales (INNODOCTO), grant INNTA3/2020/12.Moner Cano, D. (2021). Archetype development and governance methodologies for the electronic health record [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16491

Intégration de ressources en recherche translationnelle : une approche unificatrice en support des systèmes de santé "apprenants"

Learning health systems (LHS) are gradually emerging and propose a complimentary approach to translational research challenges by implementing close coupling of health care delivery, research and knowledge translation. To support coherent knowledge sharing, the system needs to rely on an integrated and efficient data integration platform. The framework and its theoretical foundations presented here aim at addressing this challenge. Data integration approaches are analysed in light of the requirements derived from LHS activities and data mediation emerges as the one most adapted for a LHS. The semantics of clinical data found in biomedical sources can only be fully derived by taking into account, not only information from the structural models (field X of table Y), but also terminological information (e.g. International Classification of Disease 10th revision) used to encode facts. The unified framework proposed here takes this into account. The platform has been implemented and tested in context of the TRANSFoRm endeavour, a European project funded by the European commission. It aims at developing a LHS including clinical activities in primary care. The mediation model developed for the TRANSFoRm project, the Clinical Data Integration Model, is presented and discussed. Results from TRANSFoRm use-cases are presented. They illustrate how a unified data sharing platform can support and enhance prospective research activities in context of a LHS. In the end, the unified mediation framework presented here allows sufficient expressiveness for the TRANSFoRm needs. It is flexible, modular and the CDIM mediation model supports the requirements of a primary care LHS.Les systèmes de santé "apprenants" (SSA) présentent une approche complémentaire et émergente aux problèmes de la recherche translationnelle en couplant de près les soins de santé, la recherche et le transfert de connaissances. Afin de permettre un flot d’informations cohérent et optimisé, le système doit se doter d’une plateforme intégrée de partage de données. Le travail présenté ici vise à proposer une approche de partage de données unifiée pour les SSA. Les grandes approches d’intégration de données sont analysées en fonction du SSA. La sémantique des informations cliniques disponibles dans les sources biomédicales est la résultante des connaissances des modèles structurelles des sources mais aussi des connaissances des modèles terminologiques utilisés pour coder l’information. Les mécanismes de la plateforme unifiée qui prennent en compte cette interdépendance sont décrits. La plateforme a été implémentée et testée dans le cadre du projet TRANSFoRm, un projet européen qui vise à développer un SSA. L’instanciation du modèle de médiation pour le projet TRANSFoRm, le Clinical Data Integration Model est analysée. Sont aussi présentés ici les résultats d’un des cas d’utilisation de TRANSFoRm pour supporter la recherche afin de donner un aperçu concret de l’impact de la plateforme sur le fonctionnement du SSA. Au final, la plateforme unifiée d’intégration proposée ici permet un niveau d’expressivité suffisant pour les besoins de TRANSFoRm. Le système est flexible et modulaire et le modèle de médiation CDIM couvre les besoins exprimés pour le support des activités d’un SSA comme TRANSFoRm

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Development and Evaluation of an Ontology-Based Quality Metrics Extraction System

The Institute of Medicine reports a growing demand in recent years for quality improvement within the healthcare industry. In response, numerous organizations have been involved in the development and reporting of quality measurement metrics. However, disparate data models from such organizations shift the burden of accurate and reliable metrics extraction and reporting to healthcare providers. Furthermore, manual abstraction of quality metrics and diverse implementation of Electronic Health Record (EHR) systems deepens the complexity of consistent, valid, explicit, and comparable quality measurement reporting within healthcare provider organizations. The main objective of this research is to evaluate an ontology-based information extraction framework to utilize unstructured clinical text for defining and reporting quality of care metrics that are interpretable and comparable across different healthcare institutions. All clinical transcribed notes (48,835) from 2,085 patients who had undergone surgery in 2011 at MD Anderson Cancer Center were extracted from their EMR system and pre- processed for identification of section headers. Subsequently, all notes were analyzed by MetaMap v2012 and one XML file was generated per each note. XML outputs were converted into Resource Description Framework (RDF) format. We also developed three ontologies: section header ontology from extracted section headers using RDF standard, concept ontology comprising entities representing five quality metrics from SNOMED (Diabetes, Hypertension, Cardiac Surgery, Transient Ischemic Attack, CNS tumor), and a clinical note ontology that represented clinical note elements and their relationships. All ontologies (Web Ontology Language format) and patient notes (RDFs) were imported into a triple store (AllegroGraph?) as classes and instances respectively. SPARQL information retrieval protocol was used for reporting extracted concepts under four settings: base Natural Language Processing (NLP) output, inclusion of concept ontology, exclusion of negated concepts, and inclusion of section header ontology. Existing manual abstraction data from surgical clinical reviewers, on the same set of patients and documents, was considered as the gold standard. Micro-average results of statistical agreement tests on the base NLP output showed an increase from 59%, 81%, and 68% to 74%, 91%, and 82% (Precision, Recall, F-Measure) respectively after incremental addition of ontology layers. Our study introduced a framework that may contribute to advances in “complementary” components for the existing information extraction systems. The application of an ontology-based approach for natural language processing in our study has provided mechanisms for increasing the performance of such tools. The pivot point for extracting more meaningful quality metrics from clinical narratives is the abstraction of contextual semantics hidden in the notes. We have defined some of these semantics and quantified them in multiple complementary layers in order to demonstrate the importance and applicability of an ontology-based approach in quality metric extraction. The application of such ontology layers introduces powerful new ways of querying context dependent entities from clinical texts. Rigorous evaluation is still necessary to ensure the quality of these “complementary” NLP systems. Moreover, research is needed for creating and updating evaluation guidelines and criteria for assessment of performance and efficiency of ontology-based information extraction in healthcare and to provide a consistent baseline for the purpose of comparing alternative approaches

Improving the Quality and Utility of Electronic Health Record Data through Ontologies

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs