Ratnesh Sahay

PPEPR: plug and play electronic patient records

The integration of Electronic Patient Record (EPR) systems is at the centre of many of the new regional and national initiatives to integrate clinical processes across department, region, and national levels. Web Service technologies offer significant solutions to provide an interoperable communication infrastructure but are unable to support precise definitions for healthcare messages, functionality, and standards, required for making meaningful integration. The lack of interoperability within healthcare standards adds complexity to the initiatives. This heterogeneity exists within two versions of same standard (e.g. HL7), and also between standards (e.g. HL7, openEHR, CEN TC/251 13606). We therefore introduce an integration platform PPEPR (Plug and Play Electronic Patient Records), which is based on the principles of a semantic Service-Oriented Architecture (sSOA). PPEPR solves the problem of interoperability at the semantic level. A key focus of PPEPR is that once a patient information is captured, should be available for use across all potential care processes.

A methodological approach for ontologising and aligning health level seven (HL7) applications

Healthcare applications are complex in the way data and schemas are organised in their internal systems. Widely deployed healthcare standards like Health Level Seven (HL7) V2 are designed using flexible schemas which allow several choices when constructing clinical messages. The recently emerged HL7 V3 has a centrally consistent information model that controls terminologies and concepts shared by V3 applications. V3 information models are arranged in several layers (abstract to concrete layers). V2 and V3 systems raise interoperability challenges: firstly, how to exchange clinical messages between V2 and V3 applications, and secondly, how to integrate globally defined clinical concepts with locally constructed concepts. The use of ontologies for interoperable healthcare applications has been advocated by domain and knowledge representation specialists. This paper addresses two main areas of an ontology-based integration framework: (1) an ontology building methodology for the HL7 standard where ontologies are developed in separated global and local layers; and (2) aligning V2 and V3 ontologies. We propose solutions that: (1) provide a semi-automatic mechanism to build HL7 ontologies; (2) provide a semi-automatic mechanism to align HL7 ontologies and transform underlying clinical messages. The proposed methodology has developed HL7 ontologies of 300 concepts in average for each version. These ontologies and their alignments are deployed and evaluated under a semantically-enabled healthcare integration framework.

On-the-fly generation of multidimensional data cubes for web of things

The dynamicity of sensor data sources and publishing real-time sensor data over a generalised infrastructure like the Web pose a new set of integration challenges. Semantic Sensor Networks demand excessive expressivity for efficient formal analysis of sensor data. This article specifically addresses the problem of adapting data model specific or context-specific properties in automatic generation of multidimensional data cubes. The idea is to generate data cubes on-the-fly from syntactic sensor data to sustain decision making, event processing and to publish this data as Linked Open Data.

SAFE: SPARQL federation over RDF data cubes with access control

BackgroundSeveral query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data.ResultsWe present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change.ConclusionsWe validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

An Ontology for Clinical Trial Data Integration

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers, and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHRs usability in clinical trials.

Heterogeneity and Context in Semantic-Web-Enabled HCLS Systems

The need for semantics preserving integration of complex data has been widely recognized in the healthcare domain. While standards such as Health Level Seven (HL7) have been developed in this direction, they have mostly been applied in limited, controlled environments, still being used incoherently across countries, organizations, or hospitals. In a more mobile and global society, data and knowledge are going to be commonly exchanged between various systems at Web scale. Specialists in this domain have increasingly argued in favor of using Semantic Web technologies for modeling healthcare data in a well formalized way. This paper provides a reality check in how far current Semantic Web standards can tackle interoperability issues arising in such systems driven by the modeling of concrete use cases on exchanging clinical data and practices. Recognizing the insufficiency of standard OWL to model our scenario, we survey theoretical approaches to extend OWL by modularity and context towards handling heterogeneity in Semantic-Web-enabled health care and life sciences (HCLS) systems. We come to the conclusion that none of these approaches addresses all of our use case heterogeneity aspects in its entirety. We finally sketch paths on how better approaches could be devised by combining several existing techniques.

PPEPR for Enterprise Healthcare Integration

PPEPR is software to connect healthcare enterprises. Healthcare is a complex domain and any integration system that connects healthcare enterprise applications must facilitate heterogeneous healthcare systems at all levels - data, services, processes, healthcare vendors, standards, legacy systems, and new information systems, all of which must interoperate to provide healthcare services. The lack of interoperability within healthcare standards (e.g. HL7) adds complexity to the interoperability initiatives. HL7’s user base has been growing since the early 2000s. There are many interoperability issues between the widely adopted HL7 v2 and its successor, HL7 v3, in terms of consistency, data/message modeling, precision, and useability. We have proposed an integration platform called PPEPR: (Plug and Play Electronic Patient Records) which is based on a semantic Service-oriented Architecture (sSOA). PPEPR connects HL7 (v2 & v3) compliant healthcare enterprises. Our main goal is to provide seamless integration between healthcare enterprises without imposing any constraint on existing or proposed EPRs.

Advancing Patient Record Safety and EHR Semantic Interoperability

Electronic Health Records (EHRs) contain an increasing wealth of medical information, which has the potential to significantly advance medical research and health policies formulation, providing society with additional benefits within a global health perspective. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. Providing an interoperability infrastructure for EHRs is on the agenda of many regional, and international eHealth initiatives. The semantic interoperability of patient data between EHRs and medical research can transform todays process of drug discovery and development, enable faster access to effective new medications, provide improved patient outcomes, and provide a key foundation for targeted (personalized) medicines. The scope of the current paper is the description of the effort undertaken by the Linked2Safety consortium towards the development of an innovative interoperability framework, for the efficient, homogenized access to and the effective utilization of the increasing wealth of medical information contained in the EHR systems deployed and maintained at regional and/or national level across Europe.

Towards a semantic representation for multi-scale finite element biosimulation experiments

Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontology-based biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

A Formal Investigation of Semantic Interoperability of HCLS Systems

Semantic interoperability facilitates Health Care and Life Sciences (HCLS) systems in connecting stakeholders at various levels as well as ensuring seamless use of healthcare resources. Their scope ranges from local to regional, national and cross-border. The use of semantics in delivering interoperable solution for HCLS systems is weakened by fact that an Ontology Based Information System (OBIS) has restrictions in modeling, aggregating, and interpreting global knowledge in conjunction with local information (e.g., policy, profiles). This chapter presents an example-scenario that shows such limitations and recognizes that enabling two key features, namely the type and scope of knowledge, within a knowledge base could enhance the overall effectiveness of an OBIS. This chapter provides the idea of separating knowledge bases in types with scope (e.g., global or local) of applicability. Then, it proposes two concrete solutions on this general notion. Finally, the chapter describes open research issues that may be of interest to knowledge system developers and broader research community.