Non-invasive lightweight integration engine for building EHR from autonomous distributed systems

[EN] In this paper we describe Pangea-LE, a message-oriented lightweight data integration engine that allows homogeneous and concurrent access to clinical information from disperse and heterogeneous data sources. The engine extracts the information and passes it to the requesting client applications in a flexible XML format. The XML response message can be formatted on demand by appropriate Extensible Stylesheet Language (XSL) transformations in order to meet the needs of client applications. We also present a real deployment in a hospital where Pangea-LE collects and generates an XML view of all the available patient clinical information. The information is presented to healthcare professionals in an Electronic Health Record (EHR) viewer Web application with patient search and EHR browsing capabilities. Implantation in a real setting has been a success due to the non-invasive nature of Pangea-LE which respects the existing information systems.This work was partially funded by the Spanish Ministry of Science and Technology (MEC-TSI2004-06475-102-01) and the Spanish Ministry of Health (PI052245)Angulo Fernández, C.; Crespo Molina, PM.; Maldonado Segura, JA.; Moner Cano, D.; Perez Cuesta, D.; Abad, I.; Mandingorra Gimenez, J.... (2007). Non-invasive lightweight integration engine for building EHR from autonomous distributed systems. International Journal of Medical Informatics. 76(Supplement 3):417-424. https://doi.org/10.1016/j.ijmedinf.2007.05.002S41742476Supplement

An archetype-based solution for the interoperability of computerised guidelines and electronic health records

Clinical guidelines contain recommendations based on the best empirical evidence available at the moment. There is a wide con- sensus about the benefits of guidelines and about the fact that they should be deployed through clinical information systems, making them available during consultation time. However, one of the main obstacles to this integration is still the interaction with the electronic health record. In this paper we present an archetype-based approach to solve the inter- operability problems of guideline systems, as well as to enable guideline sharing. We also describe the knowledge requirements for the develop- ment of archetype-enabled guideline systems, and then focus on the de- velopment of appropriate guideline archetypes and on the connection of these archetypes to the target electronic health record

An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems

The success of Clinical Decision Support Systems (CDSS) greatly depends on its capability of being integrated in Health Information Systems (HIS). Several proposals have been published up to date to permit CDSS gathering patient data from HIS. Some base the CDSS data input on the HL7 reference model, however, they are tailored to specific CDSS or clinical guidelines technologies, or do not focus on standardizing the CDSS resultant knowledge. We propose a solution for facilitating semantic interoperability to rule-based CDSS focusing on standardized input and output documents conforming an HL7-CDA wrapper. We define the HL7-CDA restrictions in a HL7-CDA implementation guide. Patient data and rule inference results are mapped respectively to and from the CDSS by means of a binding method based on an XML binding file. As an independent clinical document, the results of a CDSS can present clinical and legal validity. The proposed solution is being applied in a CDSS for providing patient-specific recommendations for the care management of outpatients with diabetes mellitus.We thank Fagor Electrodomesticos S.Coop for their support and funding in the development of this work, specially to Juan Ramon Inurria and Jorge de Antonio Prieto. We also thank the colaboration from Universidad de Mondragon in the design of the general architecture of the telemedicine system, specially, Felix Larrinaga. This work has been partially supported by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001.Sáez Silvestre, C.; Bresó Guardado, A.; Vicente Robledo, J.; Robles Viejo, M.; García Gómez, JM. (2013). An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems. Computer Methods and Programs in Biomedicine. 109(3):239-249. doi:10.1016/j.cmpb.2012.10.003S239249109

LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics

Purpose To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data. Methods The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes. Results LinkEHR-Ed, a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts, is developed. Conclusions LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.This work was supported in part by the Spanish Ministry of Education and Science under grant TS12007-66S7S-C02; by the Generalitat Valenciana under grant APOSTD/2007/055 and by the program PAID-06-07 de la Universidad Politecnica de Valencia.Maldonado Segura, JA.; Moner Cano, D.; Boscá Tomás, D.; Fernandez Breis, JT.; Angulo Fernández, C.; Robles Viejo, M. (2009). LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics. International Journal of Medical Informatics. 78(8):559-570. https://doi.org/10.1016/j.ijmedinf.2009.03.006S55957078

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

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

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.This research has been supported by the Spanish Ministry of Education through Grant PR2010-0279, and by Universitat Jaume I through Project P1182009-38. Additionally, this research has been supported by the Spanish Ministry of Science and Innovation under Grant TIN2010-21388-C02-01, and by the Spanish Ministry of Economy and Competitiveness under grant PTQ-11-04987.Marcos, M.; Maldonado Segura, JA.; Martinez-Salvador, B.; Boscá Tomás, D.; Robles Viejo, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility. Journal of Biomedical Informatics. 46(4):676-689. https://doi.org/10.1016/j.jbi.2013.05.004S67668946

2021 - The Second Annual Fall Symposium of Student Scholars

The full program book from the Fall 2020 Symposium of Student Scholars, held on November 18, 2021. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1024/thumbnail.jp

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT