The objective of this panel is to inform the ISDS community of the progress made in the Standards & Interoperability (S&I) Framework Public Health Reporting Initiative (PHRI). Also, it will provide some context of how the initiative will likely affect biosurveillance reporting in Meaningful Use Stage 3 and future harmonization of data standards requirements for public health reporting.
The S&I Framework is an Office of National Coordinator (ONC) initiative designed to support individual working groups who focus on a specific interoperability challenge. One of these working groups within the S&I Framework is the PHRI, which is using the S&I Framework as a platform for a community-led project focused on simplifying public health reporting and ensuring EHR interoperability with public health information systems. PHRI hopes to create a new public health reporting objective for Meaningful Use Stage 3 that is broader than the current program-specific objectives and will lay the ground work for all public health reporting in the future. To date, the initiative received over 30 descriptions of different types of public health reporting that were then grouped into 5 domain categories. Each domain category was decomposed into component elements and commonalities were identified. The PHRI is now working to reconstruct a single model of public health reporting through a consensus process that will soon lead to a pilot demonstration of the most ready reporting types. This panel will outline progress, challenges, and next steps of the initiative as well as describe how the initiative may affect a standard language for biosurveillance reporting.
Michael Coletta will provide an introduction and background of the S&I PHRI. He will describe how the PHRI intends to impact reporting in a way that is universal and helpful to both HIT vendors and public health programs.
Nikolay Lipskiy will provide an understanding of the ground breaking nature of collaboration and harmonization that is occurring across public health programs. He will describe the data harmonization process, outcomes, and hopes for the future of this work.
David Birnbaum has been a very active member of PHRI and has consistently advocated for the inclusion of Healthcare Associated Infections (HAI) reporting in Meaningful Use as a model. David has been representing one of the largest user communities among those farthest along toward automated uploading of data to public health agencies. He will describe the opportunities and challenges of this initiative from the perspective of a participant representing an already highly evolved reporting system (CDC’s National Healthcare Safety Network system).
John Abellera has been the steward of the communicable disease reporting user story for the PHRI. He will describe the current challenges to reporting and how the PHRI proposed changes could improve communicable disease reporting efforts.
This will be followed by an open discussion with the audience intended to elicit reactions regarding an eventual consolidation from individual report specific specification documents to one core report specification across public health reporting programs which is then supplemented with both program specific specifications and a limited number of implementation specific specifications.
Plan to engage audience: Have a prepared list of questions to pose to the audience for reactions and discussion (to be supplied if participation is low).
Standards; Interoperability; Meaningful Use; Reporting; Stage 3
The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation.
Globalization and the advances in modern information and communication technologies (ICT) are changing the practice of health care and policy making. In the globalized economies of the 21 century, health systems will have to respond to the need of increasingly mobile citizens, patients and providers. At the same time the increased use of ICT is enabling health systems to systematize, process and integrate multiple data silos from different settings and at various levels. To meet these challenges effectively, the creation of an interoperable, global e-Health information infrastructure is critical. Data interoperability within and across heterogeneous health systems, however, is often hampered by differences in terminological inconsistencies and the lack of a common language, particularly when multiple communities of practice from different countries are involved.
Discuss the functionality and ontological requirements for ICF in achieving semantic interoperability of e-Health information systems.
Most solution attempts for interoperability to date have only focused on technical exchange of data in common formats. Automated health information exchange and aggregation is a very complex task which depends on many crucial prerequisites. The overall architecture of the health information system has to be defined clearly at macro and micro levels in terms of its building blocks and their characteristics. The taxonomic and conceptual features of the ICF make it an important architectural element in the overall design of e-Health information systems. To use the ICF in a digital environment the classification needs to be formalized and modeled using ontological principles and description logic. Ontological modeling is also required for linking assessment instruments and clinical terminologies (e.g. SNOMED) to the ICF.
To achieve semantic interoperability of e-Health systems a carefully elaborated overall health information system architecture has to be established. As a content standard, the ICF can play a pivotal role for meaningful and automated compilation and exchange of health information across sectors and levels. In order to fulfill this role a ICF ontology needs to be developed.
semantic interoperability; health and disability classification; ontology development
Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems.
The caCORE SDK requires a Unified Modeling Language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enabling technology for caBIG.
The caCORE SDK substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment. It has gained acceptance among developers in the caBIG program, and is expected to provide a common mechanism for creating data service nodes on the data grid that is under development.
Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. This brings with it the challenge of integrating phylogenetic and other related biological data found in a wide variety of formats, and underlines the need for reusable software that can read, manipulate and transform this information into the various forms required to build computational pipelines.
We built a Python software library for working with phylogenetic data that is tightly integrated with Biopython, a broad-ranging toolkit for computational biology. Our library, Bio.Phylo, is highly interoperable with existing libraries, tools and standards, and is capable of parsing common file formats for phylogenetic trees, performing basic transformations and manipulations, attaching rich annotations, and visualizing trees. We unified the modules for working with the standard file formats Newick, NEXUS and phyloXML behind a consistent and simple API, providing a common set of functionality independent of the data source.
Bio.Phylo meets a growing need in bioinformatics for working with heterogeneous types of phylogenetic data. By supporting interoperability with multiple file formats and leveraging existing Biopython features, this library simplifies the construction of phylogenetic workflows. We also provide examples of the benefits of building a community around a shared open-source project. Bio.Phylo is included with Biopython, available through the Biopython website, http://biopython.org.
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.
The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.
Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.
One of the requirements for a federated information system is interoperability, the ability of one computer system to access and use the resources of another system. This feature is particularly important in biomedical research systems, which need to coordinate a variety of disparate types of data. In order to meet this need, the National Cancer Institute Center for Bioinformatics (NCICB) has created the cancer Common Ontologic Representation Environment (caCORE), an interoperability infrastructure based on Model Driven Architecture. The caCORE infrastructure provides a mechanism to create interoperable biomedical information systems. Systems built using the caCORE paradigm address both aspects of interoperability: the ability to access data (syntactic interoperability) and understand the data once retrieved (semantic interoperability). This infrastructure consists of an integrated set of three major components: a controlled terminology service (Enterprise Vocabulary Services), a standards-based metadata repository (the cancer Data Standards Repository) and an information system with an Application Programming Interface (API) based on Domain Model Driven Architecture. This infrastructure is being leveraged to create a Semantic Service Oriented Architecture (SSOA) for cancer research by the National Cancer Institute’s cancer Biomedical Informatics Grid (caBIG™).
Semantic Interoperability; Model Driven Architecture; Metadata; Controlled Terminology; ISO 11179
The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research.
The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams.
The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases.
The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program.
The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.
Collaborative Digital Anatomic Pathology refers to the use of information technology that supports the creation and sharing or exchange of information, including data and images, during the complex workflow performed in an Anatomic Pathology department from specimen reception to report transmission and exploitation. Collaborative Digital Anatomic Pathology can only be fully achieved using medical informatics standards. The goal of the international integrating the Healthcare Enterprise (IHE) initiative is precisely specifying how medical informatics standards should be implemented to meet specific health care needs and making systems integration more efficient and less expensive.
To define the best use of medical informatics standards in order to share and exchange machine-readable structured reports and their evidences (including whole slide images) within hospitals and across healthcare facilities.
Specific working groups dedicated to Anatomy Pathology within multiple standards organizations defined standard-based data structures for Anatomic Pathology reports and images as well as informatic transactions in order to integrate Anatomic Pathology information into the electronic healthcare enterprise.
The DICOM supplements 122 and 145 provide flexible object information definitions dedicated respectively to specimen description and Whole Slide Image acquisition, storage and display. The content profile “Anatomic Pathology Structured Report” (APSR) provides standard templates for structured reports in which textual observations may be bound to digital images or regions of interest. Anatomic Pathology observations are encoded using an international controlled vocabulary defined by the IHE Anatomic Pathology domain that is currently being mapped to SNOMED CT concepts.
Recent advances in standards for Collaborative Digital Anatomic Pathology are a unique opportunity to share or exchange Anatomic Pathology structured reports that are interoperable at an international level. The use of machine-readable format of APSR supports the development of decision support as well as secondary use of Anatomic Pathology information for epidemiology or clinical research.
Background: Recent years have seen a surge in projects that produce large volumes of structured, machine-readable biodiversity data. To make these data amenable to processing by generic, open source “data enrichment” workflows, they are increasingly being represented in a variety of standards-compliant interchange formats. Here, we report on an initiative in which software developers and taxonomists came together to address the challenges and highlight the opportunities in the enrichment of such biodiversity data by engaging in intensive, collaborative software development: The Biodiversity Data Enrichment Hackathon.
Results: The hackathon brought together 37 participants (including developers and taxonomists, i.e. scientific professionals that gather, identify, name and classify species) from 10 countries: Belgium, Bulgaria, Canada, Finland, Germany, Italy, the Netherlands, New Zealand, the UK, and the US. The participants brought expertise in processing structured data, text mining, development of ontologies, digital identification keys, geographic information systems, niche modeling, natural language processing, provenance annotation, semantic integration, taxonomic name resolution, web service interfaces, workflow tools and visualisation. Most use cases and exemplar data were provided by taxonomists.
One goal of the meeting was to facilitate re-use and enhancement of biodiversity knowledge by a broad range of stakeholders, such as taxonomists, systematists, ecologists, niche modelers, informaticians and ontologists. The suggested use cases resulted in nine breakout groups addressing three main themes: i) mobilising heritage biodiversity knowledge; ii) formalising and linking concepts; and iii) addressing interoperability between service platforms. Another goal was to further foster a community of experts in biodiversity informatics and to build human links between research projects and institutions, in response to recent calls to further such integration in this research domain.
Conclusions: Beyond deriving prototype solutions for each use case, areas of inadequacy were discussed and are being pursued further. It was striking how many possible applications for biodiversity data there were and how quickly solutions could be put together when the normal constraints to collaboration were broken down for a week. Conversely, mobilising biodiversity knowledge from their silos in heritage literature and natural history collections will continue to require formalisation of the concepts (and the links between them) that define the research domain, as well as increased interoperability between the software platforms that operate on these concepts.
Biodiversity informatics; Data enrichment; Hackathon; Intelligent openness; Linked data; Open source; Software; Semantic Web; Taxonomy; Web services
Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications.
This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources.
A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers.
We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: 1) Determine if any contradictory knowledge exists in LOINC. 2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. 3) Provide suggestions for improving the semantic interoperability of LOINC.
We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness.
The number of LOINC codes evaluated were 1,917, 1,267 and 1,693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2,022, 2,030, and 2,301 version spaces among ARUP & Intermountain, Intermountain & Regenstrief and ARUP & Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1,165, 765 and 1,121 semantically interoperable pairs. The interoperable pairs were classified into three levels: 1) Level I – No loss of meaning, complete information was exchanged by identical codes. 2) Level II – No loss of meaning, but processing of data was needed to make the data completely comparable. 3) Level III – Some loss of meaning. For example, tests with a specific ‘method’ could be rolled-up with tests that were ‘methodless’.
There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange.
Controlled Vocabulary; LOINC; Evaluation Research; Clinical Laboratory Information Systems; LOINC Usage; Consistency; Usefulness; Semantic interoperability
To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance.
The use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary.
Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test.
Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice.
To characterize the use of standardized vocabularies in electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, we analyzed ELR messages from two states: Indiana and Wisconsin. We examined the data in the ELR messages where tests and results are reported (3). For each field, the proportion of field values that used either LOINC or SNOMED CT codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields.
Results are summarized in Table-1. In Indiana, less than 17% of incoming ELR messages contained a standardized code for identifying the test performed by the laboratory, and none of the test result fields contained a standardized vocabulary concept. For Wisconsin, none of the incoming ELR messages contained a standardized code for identifying the test performed, and less than 13% of the test result fields contained a SNOMED CT concept.
Although Wisconsin and Indiana both have high adoption of advanced health information systems with many hospitals and laboratories using commercial systems which claim to support interoperability, very few ELR messages emanate from real-world systems with interoperable codes to identify tests and clinical results. To effectively use the arriving ELR messages, Indiana and Wisconsin health departments employ software and people workarounds to translate the incoming data into standardized concepts that can be utilized by the states’ surveillance systems. These workarounds present challenges for budget constrained public health departments seeking to leverage Meaningful Use Certified technologies to improve notifiable disease surveillance.
Standards; Interoperability; Electronic Laboratory Reporting; Public Health Surveillance; Computerized Medical Records Systems
Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions.
A standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process.
Four new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically.
The quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions.
This case study provides evidence that both community- and tool-enabled review processes, structured in the Clinical Knowledge Manager, ensure archetype quality. It offers a pragmatic but feasible way to reduce variation in the representation of clinical information models towards a more unified and interoperable model.
Electronic health records; Multiple sclerosis; Medical documentation; Information models; Archetypes
Network Tools and Applications in Biology (NETTAB) Workshops are a series of meetings focused on the most promising and innovative ICT tools and to their usefulness in Bioinformatics. The NETTAB 2011 workshop, held in Pavia, Italy, in October 2011 was aimed at presenting some of the most relevant methods, tools and infrastructures that are nowadays available for Clinical Bioinformatics (CBI), the research field that deals with clinical applications of bioinformatics.
In this editorial, the viewpoints and opinions of three world CBI leaders, who have been invited to participate in a panel discussion of the NETTAB workshop on the next challenges and future opportunities of this field, are reported. These include the development of data warehouses and ICT infrastructures for data sharing, the definition of standards for sharing phenotypic data and the implementation of novel tools to implement efficient search computing solutions.
Some of the most important design features of a CBI-ICT infrastructure are presented, including data warehousing, modularity and flexibility, open-source development, semantic interoperability, integrated search and retrieval of -omics information.
Clinical Bioinformatics goals are ambitious. Many factors, including the availability of high-throughput "-omics" technologies and equipment, the widespread availability of clinical data warehouses and the noteworthy increase in data storage and computational power of the most recent ICT systems, justify research and efforts in this domain, which promises to be a crucial leveraging factor for biomedical research.
Centralized silos of genomic data are architecturally easier to initially design, develop and deploy than distributed models. However, as interoperability pains in EHR/EMR, HIE and other collaboration-centric life sciences domains have taught us, the core challenge of networking genomics systems is not in the construction of individual silos, but the interoperability of those deployments in a manner embracing the heterogeneous needs, terms and infrastructure of collaborating parties. This article demonstrates the adaptation of BitTorrent to private collaboration networks in an authenticated, authorized and encrypted manner while retaining the same characteristics of standard BitTorrent.
The BitTorious portal was sucessfully used to manage many concurrent domestic Bittorrent clients across the United States: exchanging genomics data payloads in excess of 500GiB using the uTorrent client software on Linux, OSX and Windows platforms. Individual nodes were sporadically interrupted to verify the resilience of the system to outages of a single client node as well as recovery of nodes resuming operation on intermittent Internet connections.
The authorization-based extension of Bittorrent and accompanying BitTorious reference tracker and user management web portal provide a free, standards-based, general purpose and extensible data distribution system for large ‘omics collaborations.
Networking; Data transfer; Bioinformatics; Big data; Software; Open source; Bittorrent
More and more people suffer from heart failure and the expectation is that this number will only increase the coming years. Innovations are needed to keep healthcare accessible as well as affordable. Telemonitoring is one of the promising innovations that can be deployed for making the care for heart failure patients safer and more efficient. Nevertheless, the use of these eHealth solutions are not yet in proportion to our objective. There are many reasons for this situation in terms of funding, acceptance, questions about liability, etc. Another very important reason is the lack of interoperability: there is no interaction or information exchange between different systems. This leads to a situation in which information is not, or not in time, available to the care provider. A heart failure patient using telemonitoring measures his body weight, his blood pressure and answers some questions on a daily basis. Based on these data, the care providers in the hospital are able to monitor the health status of the patient over a distance. However, care providers lack access to all information on the patient in one application. The telemonitoring information can be found in the telemonitoring system, whereas the other medical information (medication overview, medical history, etc.) is stored in the hospital information system or electronic patient record. As a result, not all patient information is available in one system or it has to be copied manually, with all the consequences that can entail.
Aims and objectives
The aim of this project is to improve the information exchange and to stimulate the use and acceptance of telemonitoring. Nictiz initiated assembling all stakeholders to develop interoperability profiles that will improve the information exchange.
Methods and results
To enable interoperability, standards are a required but not sufficient condition. It is also necessary to agree on how those standards are applied to support specific care processes and to exchange the correct information at the correct moment. This can be achieved by developing interoperability profiles. In these profiles agreements between all stakeholders are recorded on process, information, application, and technical level. Starting point was the problem on information exchange described above and the needs and the interests of the stakeholders. Based on this specific use case, health care professionals, patient representatives, IT suppliers, and insurers collaborate to make agreements about interoperability between the telemonitoring system and the electronic patient record used in the hospital. This results in functional and technical design specifications, based on the Continua Design Guidelines. These profiles will be implemented in the relevant applications, resulting in an information exchange between the telemonitoring systems and the electronic patient record systems in a standardized way.
With the use of interoperability profiles defined by all stakeholders, the telemonitoring data are available in the electronic patient record of the heart failure patient used in the hospital. In this way, all information is easily available for the care providers, thereby making the care for heart failure patients safer and more efficient.
telemonitoring; chronic heart failure; interoperability; profiles
EHRs can now be adapted to integrate seamlessly with existing research platforms. However, key challenges need to be overcome in order to provide a platform that functions across many EHR systems.
IHE Quality, Research and Public Health (QRPH)
domain addresses the information exchange standards necessary to share information relevant to quality improvement in patient care and clinical research. In collaboration with CDISC’s Healthcare Link initiative, IHE QRPH has developed a set of integration profiles that specifically address EHR-enabled research.
The panel participants from three European projects will present how subsets of existing IHE QRPH profiles can be pulled together (and extended when necessary) to form a super profile which will standardize and automate the clinical trial process flow.
project is providing adaptable, reusable and scalable tools and services for reusing data from hospital EHRs for Clinical Research.
is developing an informatics infrastructure to support the learning healthcare system in European Primary Care.
project is providing scalable, standard based interoperability framework for sustainable proactive post market safety studies. Overall, the panel will discuss the key steps towards realizing a joint
EHR4CR/TRANSFoRm/SALUS European projectathon
demonstrating EHR-enabled clinical research across Europe using standard-based integration and content profiles.
Image acquisition, processing, and quantification of objects (morphometry) require the integration of data inputs and outputs originating from heterogeneous sources. Management of the data exchange along this workflow in a systematic manner poses several challenges, notably the description of the heterogeneous meta-data and the interoperability between the software used. The use of integrated software solutions for morphometry and management of imaging data in combination with ontologies can reduce meta-data loss and greatly facilitate subsequent data analysis. This paper presents an integrated information system, called LabIS. The system has the objectives to automate (i) the process of storage, annotation, and querying of image measurements and (ii) to provide means for data sharing with third party applications consuming measurement data using open standard communication protocols. LabIS implements 3-tier architecture with a relational database back-end and an application logic middle tier realizing web-based user interface for reporting and annotation and a web-service communication layer. The image processing and morphometry functionality is backed by interoperability with ImageJ, a public domain image processing software, via integrated clients. Instrumental for the latter feat was the construction of a data ontology representing the common measurement data model. LabIS supports user profiling and can store arbitrary types of measurements, regions of interest, calibrations, and ImageJ settings. Interpretation of the stored measurements is facilitated by atlas mapping and ontology-based markup. The system can be used as an experimental workflow management tool allowing for description and reporting of the performed experiments. LabIS can be also used as a measurements repository that can be transparently accessed by computational environments, such as Matlab. Finally, the system can be used as a data sharing tool.
web-service; ontology; morphometry
In Canada, federal, provincial, and territorial governments are developing an ambitious project to implement an interoperable electronic health record (EHR). Benefits for patients, healthcare professionals, organizations, and the public in general are expected. However, adoption of an interoperable EHR remains an important issue because many previous EHR projects have failed due to the lack of integration into practices and organizations. Furthermore, perceptions of the EHR vary between end-user groups, adding to the complexity of implementing this technology. Our aim is to produce a comprehensive synthesis of actual knowledge on the barriers and facilitators influencing the adoption of an interoperable EHR among its various users and beneficiaries.
First, we will conduct a comprehensive review of the scientific literature and other published documentation on the barriers and facilitators to the implementation of the EHR. Standardized literature search and data extraction methods will be used. Studies' quality and relevance to inform decisions on EHR implementation will be assessed. For each group of EHR users identified, barriers and facilitators will be categorized and compiled using narrative synthesis and meta-analytical techniques. The principal factors identified for each group of EHR users will then be validated for its applicability to various Canadian contexts through a two-round Delphi study, involving representatives from each end-user groups. Continuous exchanges with decision makers and periodic knowledge transfer activities are planned to facilitate the dissemination and utilization of research results in policies regarding the implementation of EHR in the Canadian healthcare system.
Given the imminence of an interoperable EHR in Canada, knowledge and evidence are urgently needed to prepare this major shift in our healthcare system and to oversee the factors that could affect its adoption and integration by all its potential users. This synthesis will be the first to systematically summarize the barriers and facilitators to EHR adoption perceived by different groups and to consider the local contexts in order to ensure the applicability of this knowledge to the particular realities of various Canadian jurisdictions. This comprehensive and rigorous strategy could be replicated in other settings.
The establishment of the Meaningful Use criteria has created a critical need for robust interoperability of health records. A universal definition of a personal health record (PHR) has not been agreed upon. Standardized code sets have been built for specific entities, but integration between them has not been supported. The purpose of this research study was to explore the hindrance and promotion of interoperability standards in relationship to PHRs to describe interoperability progress in this area. The study was conducted following the basic principles of a systematic review, with 61 articles used in the study. Lagging interoperability has stemmed from slow adoption by patients, creation of disparate systems due to rapid development to meet requirements for the Meaningful Use stages, and rapid early development of PHRs prior to the mandate for integration among multiple systems. Findings of this study suggest that deadlines for implementation to capture Meaningful Use incentive payments are supporting the creation of PHR data silos, thereby hindering the goal of high-level interoperability.
personal health record; interoperability; meaningful use; regional health information organization (RHIO)
Disease data sharing is important for the collaborative preparation, response, and recovery stages of disease control. Disease phenomena are strongly associated with spatial and temporal factors. Web-based Geographical Information Systems provide a real-time and dynamic way to represent disease information on maps. However, data heterogeneities, integration, interoperability, and cartographical representation are still major challenges in the health geographic fields. These challenges cause barriers in extensively sharing health data and restrain the effectiveness in understanding and responding to disease outbreaks. To overcome these challenges in disease data mapping and sharing, the senior authors have designed an interoperable service oriented architecture based on Open Geospatial Consortium specifications to share the spatio-temporal disease information.
A case study of infectious disease mapping across New Brunswick (Canada) and Maine (USA) was carried out to evaluate the proposed architecture, which uses standard Web Map Service, Styled Layer Descriptor and Web Map Context specifications. The case study shows the effectiveness of an infectious disease surveillance system and enables cross-border visualization, analysis, and sharing of infectious disease information through interactive maps and/or animation in collaboration with multiple partners via a distributed network. It enables data sharing and users' collaboration in an open and interactive manner.
In this project, we develop a service oriented architecture for online disease mapping that is distributed, loosely coupled, and interoperable. An implementation of this architecture has been applied to the New Brunswick and Maine infectious disease studies. We have shown that the development of standard health services and spatial data infrastructure can enhance the efficiency and effectiveness of public health surveillance.
Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.
We have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach.
Biomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.
With the deployments of Electronic Health Records (EHR), interoperability testing in healthcare is becoming crucial. EHR enables access to prior diagnostic information in order to assist in health decisions. It is a virtual system that results from the cooperation of several heterogeneous distributed systems. Interoperability between peers is therefore essential. Achieving interoperability requires various types of testing. Implementations need to be tested using software that simulates communication partners, and that provides test data and test plans.
In this paper we describe a software that is used to test systems that are involved in sharing medical images within the EHR. Our software is used as part of the Integrating the Healthcare Enterprise (IHE) testing process to test the Cross Enterprise Document Sharing for imaging (XDS-I) integration profile. We describe its architecture and functionalities; we also expose the challenges encountered and discuss the elected design solutions.
EHR is being deployed in several countries. The EHR infrastructure will be continuously evolving to embrace advances in the information technology domain. Our software is built on a web framework to allow for an easy evolution with web technology. The testing software is publicly available; it can be used by system implementers to test their implementations. It can also be used by site integrators to verify and test the interoperability of systems, or by developers to understand specifications ambiguities, or to resolve implementations difficulties.
Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalized-detailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems.
Materials and Methods:
We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimer's disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards.
For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimer's disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts.
The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.
e-health; medical records; information management; technology