Desarrollo de un servicio de enlace entre HL7 RIM y terminologías médicas

Hoy día, en la era post genómica, los ensayos clínicos de cáncer implican la colaboración de diversas instituciones. El análisis multicéntrico y retrospectivo requiere de métodos avanzados para garantizar la interoperabilidad semántica. En este escenario, el objetivo de los proyectos EURECA e INTEGRATE es proporcionar una infraestructura para compartir conocimientos y datos de los ensayos clínicos post genómicos de cáncer. Debido en gran parte a la gran complejidad de los procesos colaborativos de las instituciones, provoca que la gestión de una información tan heterogénea sea un desafío dentro del área médica. Las tecnologías semánticas y las investigaciones relacionadas están centradas en búsqueda de conocimiento de la información extraída, permitiendo una mayor flexibilidad y usabilidad de los datos extraidos. Debido a la falta de estándares adoptados por estas entidades y la complejidad de los datos procedentes de ensayos clínicos, una capacidad semántica es esencial para asegurar la integración homogénea de esta información. De otra manera, los usuarios finales necesitarán conocer cada modelo y cada formato de dato de las instituciones participantes en cada estudio. Para proveer de una capa de interoperabilidad semántica, el primer paso es proponer un\Common Data Model" (CDM) que represente la información a almacenar, y un \Core Dataset" que permita el uso de múltiples terminologías como vocabulario compartido. Una vez que el \Core Dataset" y el CDM han sido seleccionados, la manera en la que realizar el mapping para unir los conceptos de una terminología dada al CDM, requiere de una mecanismo especial para realizar dicha labor. Dicho mecanismo, debe definir que conceptos de diferentes vocabularios pueden ser almacenados en determinados campos del modelo de datos, con la finalidad de crear una representación común de la información. El presente proyecto fin de grado, presenta el desarrollo de un servicio que implementa dicho mecanismo para vincular elementos de las terminologías médicas SNOMED CT, LOINC y HGNC, con objetos del \Health Level 7 Reference Information Model" (HL7 RIM). El servicio propuesto, y nombrado como TermBinding, sigue las recomendaciones del proyecto TermInfo del grupo HL7, pero también se tienen en cuenta cuestiones importantes que surgen al enlazar entre las citadas terminologas y el modelo de datos planteado. En este proceso de desarrollo de la interoperabilidad semántica en ensayos clínicos de cáncer, los datos de fuentes heterogéneas tienen que ser integrados, y es requisito que se deba habilitar una interfaz de acceso homogéneo a toda esta información. Para poder hacer unificar los datos provenientes de diferentes aplicaciones y bases de datos, es esencial representar todos estos datos de una manera canónica o normalizada. La estandarización de un determinado concepto de SNOMED CT, simplifica las recomendaciones del proyecto TermInfo del grupo HL7, utilizadas para poder almacenar cada concepto en el modelo de datos. Siguiendo este enfoque, la interoperabilidad semántica es conseguida con éxito para conceptos SNOMED CT, sean o no post o pre coordinados, así como para las terminologías LOINC y HGNC. Los conceptos son estandarizados en una forma normal que puede ser usada para unir los datos al \Common Data Model" basado en el RIM de HL7. Aunque existen limitaciones debido a la gran heterogeneidad de los datos a integrar, un primer prototipo del servicio propuesto se está utilizando con éxito en el contexto de los proyectos EURECA e INTEGRATE. Una mejora en la interoperabilidad semántica de los datos de ensayos clínicos de cáncer tiene como objetivo mejorar las prácticas en oncología

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

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)

Background: Electronic rating scales represent an important resource for standardized data collection. However, the ability to exploit reasoning on rating scale data is still limited. The objective of this work is to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. We developed an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. In order to address the modeling challenges of the SARA, we propose to combine the best performances from OpenEHR clinical archetypes, guidelines and ontologies. Methods: A scaled-down version of the Human Phenotype Ontology (HPO) was built, extracting the terms that describe the SARA tests from free-text sources. This version of the HPO was then used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Each unit used the most appropriate technology to formally represent the required knowledge. Results: Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by Spinocerebellar Ataxia Type 36 (SCA36). When comparing the performance of our prototype with that of two independent experts, weighted kappa scores ranged from 0.62 to 0.86. Conclusions: The combination of archetypes, phenotype ontologies and electronic information-processing rules can be used to automate the extraction of relevant clinical knowledge from plain scores of rating scales. Our results reveal a substantial degree of agreement between the results achieved by an ontology-aware system and the human experts.This work presented in this paper was supported by the National Institute of Health Carlos III [grant no. FIS2012-PI12/00373: OntoNeurophen], FEDER for national and European funding; PEACE II, Erasmus Mundus Lot 2 Project [grant no. 2013-2443/001-001-EMA2]; and the Modern University for Business and Science (M.U.B.S)S

Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)

Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes. The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question. Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited: • A multidisciplinary approach to the transformation of health and social systems • Success factors for sustainable P5 ecosystems • AI and robotics in transformed health ecosystems • Transformed health ecosystems challenges for security, privacy and trust • Modelling digital health systems • Ethical challenges of personalized digital health • Knowledge representation and management of transformed health ecosystems Table of Contents: 04 Editorial: Managing healthcare transformation towards P5 medicine Bernd Blobel and Dipak Kalra 06 Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez 26 Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust Pekka Ruotsalainen and Bernd Blobel 36 Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov, Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra 49 EU-Funded Telemedicine Projects – Assessment of, and Lessons Learned From, in the Light of the SARS-CoV-2 Pandemic Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri, Claudia Bighin, Bernd Blobel and Mauro Giacomini 60 A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems Kerstin Denecke and Claude R. Baudoin 73 Modeling digital health systems to foster interoperability Frank Oemig and Bernd Blobel 89 Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence Diego M. López, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin 111 Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel and Stefan Schulz 126 The ethical challenges of personalized digital health Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides and Michael Rigb

Business model for healthcare interoperability services

En la situación actual donde los sistemas TI sanitarios son diversos con modelos que van desde soluciones predominantes, adoptadas y creadas por grandes organizaciones, hasta soluciones a medida desarrolladas por cualquier empresa de la competencia para satisfacer necesidades concretas. Todos estos sistemas se encuentran bajo similares presiones financieras, no sólo de las condiciones económicas mundiales actuales y el aumento de los costes sanitarios, sino también bajo las presiones de una población que ha adoptado los avances tecnológicos actuales, y demanda una atención sanitaria más personalizable a la altura de esos avances tecnológicos que disfruta en otros ámbitos. El objeto es desarrollar un modelo de negocio orientado al soporte del intercambio de información en el ámbito clínico. El objetivo de este modelo de negocio es aumentar la competitividad dentro de este sector sin la necesidad de recurrir a expertos en estándares, proporcionando perfiles técnicos cualificados menos costosos con la ayuda de herramientas que simplifiquen el uso de los estándares de interoperabilidad. Se hará uso de especificaciones abiertas ya existentes como FHIR, que publica documentación y tutoriales bajo licencias abiertas. La principal ventaja que nos encontramos es que ésta especificación presenta un giro en la concepción actual de la disposición de información clínica, vista hasta ahora como especial por el requerimiento de estándares más complejos que solucionen cualquier caso por específico que sea. Ésta especificación permite hacer uso de la información clínica a través de tecnologías web actuales (HTTP, HTML, OAuth2, JSON, XML) que todo el mundo puede usar sin un entrenamiento particular para crear y consumir esta información. Partiendo por tanto de un mercado con una integración de la información casi inexistente, comparada con otros entornos actuales, hará que el gasto en integración clínica aumente dramáticamente, dejando atrás los desafíos técnicos cuyo gasto retrocederá a un segundo plano. El gasto se centrará en las expectativas de lo que se puede obtener en la tendencia actual de la personalización de los datos clínicos de los pacientes, con acceso a los registros de instituciones junto con datos ‘sociales/móviles/big data’.---ABSTRACT---In the current situation IT health systems are diverse, with models varying from predominant solutions adopted and created by large organizations, to ad-hoc solutions developed by any company to meet specific needs. However, all these systems are under similar financial pressures, not only from current global economic conditions and increased health care costs, but also under pressure from a population that has embraced the current technological advances, and demand a more personalized health care, up to those enjoyed by technological advances in other areas. The purpose of this thesis is to develop a business model aimed at the provision of information exchange within the clinical domain. It is intended to increase competitiveness in the health IT sector without the need for experts in standards, providing qualified technical profiles less expensively with the help of tools that simplify the use of interoperability standards. Open specifications, like FHIR, will be used in order to enable interoperability between systems. The main advantage found within FHIR is that introduces a shift in the current conception of available clinical information. So far seen, the clinical information domain IT systems, as a special requirement for more complex standards that address any specific case. This specification allows the use of clinical information through existing web technologies (HTTP, HTML, OAuth2, JSON and XML), which everyone can use with no particular training to create and consume this information. The current situation in the sector is that the integration of information is almost nonexistent, compared to current trends. Spending in IT health systems will increase dramatically within clinical integration for the next years, leaving the technical challenges whose costs will recede into the background. The investment on this area will focus on the expectations of what can be obtained in the current trend of personalization of clinical data of patients with access to records of institutions with ‘social /mobile /big data’