Querying openEHR-based Electronic Health Record in the IHE XDS enviroment

This thesis presents the challenges related to adoption of electronic health record systems and use of standards to achieve functional and semantic interoperability. It focuses on the IHE XDS.b integration profile that enables interoperability of health information systems at the level of documentation handling. Semantic interoperability is presented from the aspect of structured data in exchanged documents establishing an openEHR based electronic health record system. Special attention is given to EHR search capabilities; IHE XDS.b search capabilities are limited to document metadata where content-based search is not supported. The key contribution of this work is a method for querying openEHR based Electronic Health Record in the IHE XDS environment. The conventional openEHR EHR System is extended to act as an IHE XDS Document Repository. Support for content-based queries is implemented through on-demand documents whose content is assembled using an AQL at the time of processing the retrieve request. The proposed method combines functional interoperability provided by the IHE integration profile XDS.b for the exchange of documents with semantic interoperability of openEHR based EHR system. The feasibility of the proposed method is presented on the case of a nation wide EHR system, eZdravje

Querying openEHR-based Electronic Health Record in the IHE XDS enviroment

This thesis presents the challenges related to adoption of electronic health record systems and use of standards to achieve functional and semantic interoperability. It focuses on the IHE XDS.b integration profile that enables interoperability of health information systems at the level of documentation handling. Semantic interoperability is presented from the aspect of structured data in exchanged documents establishing an openEHR based electronic health record system. Special attention is given to EHR search capabilities; IHE XDS.b search capabilities are limited to document metadata where content-based search is not supported. The key contribution of this work is a method for querying openEHR based Electronic Health Record in the IHE XDS environment. The conventional openEHR EHR System is extended to act as an IHE XDS Document Repository. Support for content-based queries is implemented through on-demand documents whose content is assembled using an AQL at the time of processing the retrieve request. The proposed method combines functional interoperability provided by the IHE integration profile XDS.b for the exchange of documents with semantic interoperability of openEHR based EHR system. The feasibility of the proposed method is presented on the case of a nation wide EHR system, eZdravje

Patientenübergreifende, multiple Verwendung von Patientendaten für die klinische Forschung unter Nutzung von Archetypen

Sowohl in der Routineversorgung als auch in klinischen Studien werden immer mehr Daten elektronisch verarbeitet. Trotzdem ist ein Austausch von Daten zwischen beiden Bereichen häufig noch nicht etabliert. Dies führt dazu, dass Daten mehrfach erfasst werden müssen. Die redundante Datenerfassung ist zeitaufwändig und kann zu Inkonsistenzen zwischen Krankenhausinformationssystem (KIS) und Studiendatenmanagementsystem (SDMS) führen. Obwohl ein Datenaustausch zwischen Forschung und Versorgung oft technisch möglich wäre, scheitert er meist noch an mangelnder semantischer Interoperabilität. Archetypen sind ein innovatives Konzept zur Gestaltung von flexiblen und leicht erweiterbaren elektronischen Gesundheitsakten. Sie ermöglichen semantische Interopera-bilität zwischen Systemen, welche dieselben Archetypen nutzen. Das Archetypen-Konzept hat mittlerweile auch Eingang in internationale Standards gefunden (ISO 13606). Die openEHR-Spezifikationen definieren ein mit ISO 13606 kompatibles jedoch weiter-gehendes Modell für elektronische Gesundheitsakten. Bisher wurden Archetypen hauptsächlich für Informationssysteme in der Routineversorgung und weniger für die klinische Forschung entwickelt und genutzt. Ziel dieser Arbeit war es daher, basierend auf den openEHR-Spezifikationen und Archetypen generische Ansätze zu erarbeiten, die eine multiple Verwendung von Daten aus der Versorgung in der Forschung ermöglichen und deren Umsetzbarkeit zu prüfen. In einer Voruntersuchung wurde ermittelt, dass 35 % der in der betrachteten Studie zu erhebenden Merkmalsarten aus dem untersuchten KIS übernommen werden könnten, wenn die Daten dort elektronisch und ausreichend strukturiert vorlägen. In einem zweiten Schritt wurde mit openSDMS der Prototyp eines auf Archetypen basierenden integrierten elektronischen Gesundheitsakten- und Studiendatenmanagementsystems zur Verfügung gestellt. Aus der Voruntersuchung und der Implementierung von openSDMS wurden Anforderungen abgeleitet und eine auf openEHR-Archetypen basierende Referenzarchitektur entwickelt, welche die Nutzung von Daten aus KIS in klinischen Studien unterstützt. Dabei wird sowohl die Integration von KIS beschrieben, die auf Archetypen basieren, als auch von klassischen KIS. Kernkomponenten dieser Architektur sind auf Archetypen basierende semantische Annotationen von Studiendaten sowie Import- und Exportmodule, welche die Archetype Query Language nutzen. Die vorgestellte Referenzarchitektur ermöglicht den Übergang von der multiplen Erfassung hin zur multiplen Verwendung von Daten in Forschung und Versorgung. Um die entwickelte Referenzarchitektur realisieren zu können, werden geeignete Archetypen auch für Forschungsdaten benötigt. Daher wurden Archetypen zur Dokumentation aller Datenelemente der vier CDASH Domänen ‚Common Identifier Variables‘, ‚Common Timing Variables‘, ‚Adverse Events‘ sowie ‚Prior and Concomitant Medications‘ spezifiziert (Studiendaten). Hierzu wurden insgesamt 23 Merkmalsarten basierend auf Archetypen neu definiert, wozu drei bestehende Archetypen spezialisiert und zwei neu entwickelt wurden. Zur Definition von CDASH-konformen elektronischen Datenerhebungsbogen für die betrachteten Domänen wurden, basierend auf den spezifizierten Archetypen, vier openEHR-Templates entworfen. Ferner wurden 71 Merkmalsarten in 16 Archetypen zur Dokumentation von Studien-Metadaten definiert. Alle neu entworfenen Archetypen wurden jeweils in englischer und deutscher Sprache beschrieben und können nun als Referenzinformationsmodell für Forschungsdaten genutzt werden. Ergänzend wurden alle von den bereitgestellten Archetypen definierten Merkmalsarten auf die im Bereich der klinischen Forschung etablierten Modelle BRIDG, CDASH und ODM abgebildet

Quality framework for semantic interoperability in health informatics: definition and implementation

Aligned with the increased adoption of Electronic Health Record (EHR) systems, it is recognized that semantic interoperability provides benefits for promoting patient safety and continuity of care. This thesis proposes a framework of quality metrics and recommendations for developing semantic interoperability resources specially focused on clinical information models, which are defined as formal specifications of structure and semantics for representing EHR information for a specific domain or use case. This research started with an exploratory stage that performed a systematic literature review with an international survey about the clinical information modelling best practice and barriers. The results obtained were used to define a set of quality models that were validated through Delphi study methodologies and end user survey, and also compared with related quality standards in those areas that standardization bodies had a related work programme. According to the obtained research results, the defined framework is based in the following models: Development process quality model: evaluates the alignment with the best practice in clinical information modelling and defines metrics for evaluating the tools applied as part of this process. Product quality model: evaluates the semantic interoperability capabilities of clinical information models based on the defined meta-data, data elements and terminology bindings. Quality in use model: evaluates the suitability of adopting semantic interoperability resources by end users in their local projects and organisations. Finally, the quality in use model was implemented within the European Interoperability Asset register developed by the EXPAND project with the aim of applying this quality model in a broader scope to contain any relevant material for guiding the definition, development and implementation of interoperable eHealth systems in our continent. Several European projects already expressed interest in using the register, which will now be sustained by the European Institute for Innovation through Health Data

Post-MAPS: An interactive acquisition platform for gastroenterology

Post-MAPS is a web platform that collects gastroenterological exam data from several european hospital centers, to be used in future clinical studies and was developed in partnership with experts from the gastroenterological area and information technology (IT) technicians. However, although functional, this platform has some issues that are crucial for its functioning, and can render user interaction unpleasant and exhaustive. Accordingly, we proposed the development of a new web platform, in which we aimed for an improvement in terms of usability, data uni cation and interoperability. Therefore, it was necessary to identify and study different ways of acquiring clinical data and review some of the existing clinical databases in order to understand how they work and what type of data they store, as well as their impact and contribution to clinical knowledge. Closely linked to the data model is the ability to share data with other systems, so, we also studied the concept of interoperability and analyzed some of the most widely used international standards, such as DICOM, HL7 and openEHR. As one of the primary objectives of this project was to achieve a better level of usability, practices related to Human Computer-Interaction, such as requirement analysis, creation of conceptual models, prototyping, and evaluation were also studied. Before we began the development, we conducted an analysis of the previous platform, from a functional point of view, which allowed us to gather not only a list of architectural and interface issues, but also a list of improvement opportunities. It was also performed a small preliminary study in order to evaluate the platform's usability, where we were able to realize that perceived usability is different between users, and that, in some aspects, varies according to their location, age and years of experience. Based on the information gathered during the platform's analysis and in the conclusions of the preliminary study, a new platform was developed, prepared for all potential users, from the inexperienced to the most comfortable with technology. It presents major improvements in terms of usability, also providing several new features that simplify the users' work, improving their interaction with the system, making their experience more enjoyable

The Secondary Use of Longitudinal Critical Care Data

Aims To examine the strengths and limitations of a novel United Kingdom (UK) critical care data resource that repurposes routinely collected physiological data for research. Exemplar clinical research studies will be developed to explore the unique longitudinal nature of the resource. Objectives - To evaluate the suitability of the National Institute for Health Research (NIHR) Critical Care theme of the Health Informatics Collaborative (CCHIC) data model as a representation of the Electronic Health Record (EHR) for secondary research use. - To conduct a data quality evaluation of data stored within the CC-HIC research database. - To use the CC-HIC research database to conduct two clinical research studies that make use of the longitudinal data supported by the CC-HIC: - The association between cumulative exposure to excess oxygen and outcomes in the critically ill. - The association between different morphologies of longitudinal physiology—in particular organ dysfunction—and outcomes in sepsis. The CC-HIC The EHR is now routinely used for the delivery of patient care throughout the United Kingdom (UK). This has presented the opportunity to learn from a large volume of routinely collected data. The CC-HIC data model represents 255 distinct clinical concepts including demographics, outcomes and granular longitudinal physiology. This model is used to harmonise EHR data of 12 contributing Intensive Care Units (ICUs). This thesis evaluates the suitability of the CC-HIC data model in this role and the quality of data within. While representing an important first step in this field, the CC-HIC data model lacks the necessary normalisation and semantic expressivity to excel in this role. The quality of the CC-HIC research database was variable between contributing sites. High levels of missing data, missing meta-data, non-standardised units and temporal drop out of submitted data are amongst the most challenging features to tackle. It is the principal finding of this thesis that the CC-HIC should transition towards implementing internationally agreed standards for interoperability. Exemplar Clinical Studies Two exemplar studies are presented, each designed to make use of the longitudinal data made available by the CC-HIC and address domains that are both contemporaneous and of importance to the critical care community. Exposure to Excess Oxygen Longitudinal data from the CC-HIC cohort were used to explore the association between the cumulative exposure to excess oxygen and outcomes in the critically ill. A small (likely less than 1% absolute risk reduction) dose-independent association was found between exposure to excess oxygen and mortality. The lack of dosedependency challenges a causal interpretation of these findings. Physiological Morphologies in Sepsis The joint modelling paradigm was applied to explore the different longitudinal profiles of organ failure in sepsis, while accounting for informative censoring from patient death. The rate of change of organ failure was found to play a more significan't role in outcomes than the absolute value of organ failure at a given moment. This has important implications for how the critical care community views the evolution of physiology in sepsis. DECOVID The Decoding COVID-19 (DECOVID) project is presented as future work. DECOVID is a collaborative data sharing project that pools clinical data from two large NHS trusts in England. Many of the lessons learnt from the prior work with the CC-HIC fed into the development of the DECOVID data model and its quality evaluation