Archetype-based Electronic Health Record (EHR) systems using generic reference models from e.g. openEHR, ISO 13606 or CIMI should be easy to update and reconfigure with new types (or versions) of data models or entries, ideally with very limited programming or manual database tweaking. Exploratory research (e.g. epidemiology) leading to ad-hoc querying on a population-wide scale can be a challenge in such environments. This publication describes implementation and test of an archetype-aware Dewey encoding optimization that can be used to produce such systems in environments supporting relational operations, e.g. RDBMs and distributed map-reduce frameworks like Hadoop. Initial testing was done using a nine-node 2.2 GHz quad-core Hadoop cluster querying a dataset consisting of targeted extracts from 4+ million real patient EHRs, query results with sub-minute response time were obtained.

Det är glädjande att myndigheter nu äntligen tittar mer på internationellt delade detaljerade dokumentationsmodeller för innehåll i journaler. Vi hoppas att de ger tillräckligt kraftfulla och tydliga budskap så att de upphandlande vårdgivarna också ser vikten av detta. Om vi ska bli världsbäst på eHälsa krävs internationellt samarbete, skriver flera forskare i medicinsk informatik.

Approaches such as ISO 13606 and openEHR aim to address data reusability by defining clinical data structures called archetypes and templates, based on a reference model. A problem with these approaches is that parts of them currently are rather difficult to learn. It can be hard to imagine what an archetype-based clinical system combined with modern terminology systems will look like and what consequences different modeling choices have, without seeing and experimenting with an operational system. This paper reports findings from a survey among openEHR learners and educators combined with observations of related openEHR mailing list discussions. The paper ends with an opinion piece, where we discuss potentially fruitful ways to learn, explore, and extend archetype-based EHR systems using visualization and examples.The findings highlight potential stumble blocks and solutions and should be of interest for both educators and self-learners.

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Inconsistent use of SNOMED CT concepts may reduce comparability of information in health information systems. Terminology implementation should be approached by common strategies for navigating and selecting proper concepts. This study aims to explore ways of illustrating common pathways and ancestors of particular sets of concepts, to support consistent use of SNOMED CT and also assess potential applications for such visualizations. The open source prototype presented is an interactive web-based re-implementation of the terminology visualization tool TermViz that provides an overview of concepts and their hierarchical relations. It provides terminological features such as interactively rearranging graphs, fetching more concept nodes, highlighting least common parents and shared pathways in merged graphs etc. Four teams of three to four people used the prototype to complete a terminology mapping task and then, in focus group interviews, discussed the user experience and potential future tool usage. Potential purposes discussed included SNOMED CT search and training, consistent selection of concepts and content management. The evaluation indicated that the tool may be useful in many contexts especially if integrated with existing systems, and that the graph layout needs further tuning and development.

Background

The openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content.

The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored.

Results

The main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. The modular design makes it possible to prototype, test, replicate, distribute, cache, and load-balance the system using ordinary web technology. Other contributions are approaches to query and retrieval of the EHR content that takes caching, logging, and distribution into account. Triggering on EHR change events is also explored.

A final contribution is an open source openEHR implementation using the above-mentioned approaches to create LiU EEE, an educational EHR environment intended to help newcomers and developers experiment with and learn about the archetype-based EHR approach and enable rapid prototyping.

Conclusions

Using REST addressed many architectural concerns in a successful way, but an additional messaging component was needed to address some architectural aspects. Many of our approaches are likely of value to other archetype-based EHR implementations and may contribute to associated service model specifications.

This work is a small contribution to the greater goal of making software systems used in healthcare more useful and sustainable. To come closer to that goal, health record data will need to be more computable and easier to exchange between systems.

Interoperability refers to getting systems to work together and semantics concerns the study of meanings. If Semantic interoperability is achieved then information entered in one information system is usable in other systems and reusable for many purposes. Scalability refers to the extent to which a system can gracefully grow by adding more resources. Sustainability refers more to how to best use available limited resources. Both aspects are important.

The main focus and aim of the thesis is to increase knowledge about how to support scalability and semantic sustainability. It reports explorations of how to apply aspects of the above to Electronic Health Record (EHR) systems, associated infrastructure, data structures, terminology systems, user interfaces and their mutual boundaries.

Using terminology systems is one way to improve computability and comparability of data. Modern complex ontologies and terminology systems can contain hundreds of thousands of concepts that can have many kinds of relationships to multiple other concepts. This makes visualization challenging. Many visualization approaches designed to show the local neighbourhood of a single concept node do not scale well to larger sets of nodes. The interactive TermViz approach described in this thesis, is designed to aid users to navigate and comprehend the context of several nodes simultaneously. Two applications are presented where TermViz aids management of the boundary between EHR data structures and the terminology system SNOMED CT.

The amount of available time from people skilled in health informatics is limited. Adequate methods and tools are required to develop, maintain and reuse health-IT solutions in a sustainable way. Multiple levels of modelling including a fixed reference model and another layer of flexible reusable ‘archetypes’ for domain specific data structures, is an approach with that aim used in openEHR and the ISO 13606 standard. This approach, including learning, implementing and managing it, is explored from different angles in this thesis. An architecture applying Representational State Transfer (REST) to archetype-based EHR systems, in order to address scalability, is presented. Combined with archetyping this architecture also aims at enabling a sustainable way of continuously evolving multi-vendor EHR solutions. An experimental open source implementation of it, aimed for learning and prototyping, is also presented.

Manually changing database structures used for storage every time new versions of archetypes and associated data structures are needed is likely not a sustainable activity. Thus storage systems that can handle change with minimal manual interventions are desirable. Initial explorations of performance and scalability in such systems are also reported

Graphical user interfaces focused on EHR navigation, time-perspectives and highlighting of EHR content are also presented – illustrating what can be done with computable health record data and the presented approaches.

Desirable aspects of semantic sustainability have been discussed, including: sustainable use of limited resources (such as available time of skilled people), and reduction of unnecessary risks. A semantic sustainability perspective should be inspired and informed by research in complex systems theory, and should also include striving to be highly aware of when and where technical debt is being built up. Semantic sustainability is a shared responsibility.

The combined results presented contribute to increasing knowledge about ways to support scalability and semantic sustainability in the context of electronic health record systems. Supporting tools, architectures and approaches are additional contributions.

Syftet med denna avhandling är ytterst att göra informationssystem som används i hälso- och sjukvård, särskilt patientjournaler, mer användbara och lättarbetade. Om systemen vore lättare att utveckla och underhålla skulle fler resurser kunna läggas på att tillföra nya och mer användarvänliga funktioner.

Om journalsystem och datorprogram kan ”begripa” vad olika saker i journalen är och betyder så kan de vara till större hjälp, t.ex. genom att visa bättre patientöversikter och bidra med beslutsstöd. En del i att göra journalinnehållet begripligt och hanterbart för datorer är att använda sig av terminologisystem som t.ex. ICD-10 och SNOMED CT. En annan viktig del är datastrukturerna där man stoppar in text, mätvärden, koderna från terminologisystem etc. De flesta journalsystem har någon sorts mallar som datastrukturer. Projektet openEHR har tagit fram ett sätt att dela specifikationer av datastrukturer mellan olika journalsystem så att man lättare kan dela och återanvända dem och den journaldata som matats in i dem. Dessa specifikationer kallas ”arketyper” och arketyp-metoden beskrivs även i standarden ISO 13606.

Om två olika journalsystem använder samma datastruktur, t.ex. med hjälp av samma arketyper, så kan de utväxla patientdata mellan varandra (de uppnår s.k. semantisk interoperabilitet). Begreppet ”Semantic sustainability” definieras i avhandlingen som ett förhållningssätt som är bredare än semantisk interoperabilitet. Det syftar till att möjliggöra långsiktigt hållbar utveckling av semantik (betydelse) i journalsystem och genom att hantera risker och resurser förståndigt. Förhållningssättet baserar sig på forskning och erfarenheter från systemutveckling och hantering av komplexa system och är avsett att stödja beslutsfattare, och de som utvecklar och underhåller journalsystem, relaterade system och strukturer.

För att datorsystem ska kunna växa vid ökad användning ,utan att hamna i återvändsgränder avseende prestanda, så bör vissa designprinciper för skalbarhet följas. Avhandlingen presenterar en systemarkitektur baserad på sådana principer och på arketyp-metoden. Denna arkitektur gör det möjligt att bygga system med delsystem från flera olika leverantörer. Skalbarheten i några lagringslösningar redovisas också.

Slutligen redovisas prototyper av gränssnitt för patientöversikter och journalläsning.

There are very few published studies regarding the performance of persistence mechanisms for systems that use the openEHR multi level modelling approach. This paper addresses the performance and size of XML databases that store openEHR compliant documents. Database size and response times to epidemiological queries are described. An anonymized relational epidemiology database and associated epidemiological queries were used to generate openEHR XML documents that were stored and queried in four opensource XML databases. The XML databases were considerably slower and required much more space than the relational database. For population-wide epidemiological queries the response times scaled in order of magnitude at the same rate as the number of records (total database size) but were orders of magnitude slower than the original relational database. For individual focused clinical queries where patient ID was specified the response times were acceptable. This study suggests that the tested XML database configurations without further optimizations are not suitable as persistence mechanisms for openEHR-based systems in production if population-wide ad hoc querying is needed.

Background: Exchange of Electronic Health Record (EHR) data between systems from different suppliers is a major challenge. EHR communication based on archetype methodology has been developed by openEHR and CEN/ISO. The experience of using archetypes in deployed EHR systems is quite limited today. Currently deployed EHR systems with large user bases have their own proprietary way of representing clinical content using various models. This study was designed to investigate the feasibility of representing EHR content models from a regional EHR system as openEHR archetypes and inversely to convert archetypes to the proprietary format. Methods: The openEHR EHR Reference Model (RM) and Archetype Model (AM) specifications were used. The template model of the Cambio COSMIC, a regional EHR product from Sweden, was analyzed and compared to the openEHR RM and AM. This study was focused on the convertibility of the EHR semantic models. A semantic mapping between the openEHR RM/AM and the COSMIC template model was produced and used as the basis for developing prototype software that performs automated bidirectional conversion between openEHR archetypes and COSMIC templates. Results: Automated bi-directional conversion between openEHR archetype format and COSMIC template format has been achieved. Several archetypes from the openEHR Clinical Knowledge Repository have been imported into COSMIC, preserving most of the structural and terminology related constraints. COSMIC templates from a large regional installation were successfully converted into the openEHR archetype format. The conversion from the COSMIC templates into archetype format preserves nearly all structural and semantic definitions of the original content models. A strategy of gradually adding archetype support to legacy EHR systems was formulated in order to allow sharing of clinical content models defined using different formats. Conclusion: The openEHR RM and AM are expressive enough to represent the existing clinical content models from the template based EHR system tested and legacy content models can automatically be converted to archetype format for sharing of knowledge. With some limitations, internationally available archetypes could be converted to the legacy EHR models. Archetype support can be added to legacy EHR systems in an incremental way allowing a migration path to interoperability based on standards.