Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians.

Improving the efficiency with which clinical research studies are conducted can lead to faster medication innovation and decreased time to market for new drugs. To increase this efficiency, the parties involved in a regulated clinical research study, namely, the sponsor, the clinical investigator and the regulatory body, each with their own software applications, need to exchange data seamlessly. However, currently, the clinical research and the clinical care domains are quite disconnected because each use different standards and terminology systems. In this article, we describe an initial implementation of the Semantic Framework developed within the scope of SALUS project to achieve interoperability between the clinical research and the clinical care domains. In our Semantic Framework, the core ontology developed for semantic mediation is based on the shared conceptual model of both of these domains provided by the BRIDG initiative. The core ontology is then aligned with the extracted semantic models of the existing clinical care and research standards as well as with the ontological representations of the terminology systems to create a model of meaning for enabling semantic mediation. Although SALUS is a research and development effort rather than a product, the current SALUS knowledge base contains around 4.7 million triples representing BRIDG DAM, HL7 CDA model, CDISC standards and several terminology ontologies. In order to keep the reasoning process within acceptable limits without sacrificing the quality of mediation, we took an engineering approach by developing a number of heuristic mechanisms. The results indicate that it is possible to build a robust and scalable semantic framework with a solid theoretical foundation for achieving interoperability between the clinical research and clinical care domains.

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Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

SALUS platform's interoperability solutions enable partial automation of the AE reporting process, which could contribute to improve current spontaneous reporting practices and reduce under-reporting, which is currently one major obstacle in the process of acquisition of pharmacovigilance data.

Medical devices are essential to the practice of modern healthcare services. Their benefits will increase if clinical software applications can seamlessly acquire the medical device data. The need to represent medical device observations in a format that can be consumable by clinical applications has already been recognized by the industry. Yet, the solutions proposed involve bilateral mappings from the ISO/IEEE 11073 Domain Information Model (DIM) to specific message or document standards. Considering that there are many different types of clinical applications such as the electronic health record and the personal health record systems, the clinical workflows, and the clinical decision support systems each conforming to different standard interfaces, detailing a mapping mechanism for every one of them introduces significant work and, thus, limits the potential health benefits of medical devices. In this paper, to facilitate the interoperability of clinical applications and the medical device data, we use the ISO/IEEE 11073 DIM to derive an HL7 v3 Refined Message Information Model (RMIM) of the medical device domain from the HL7 v3 Reference Information Mode (RIM). This makes it possible to trace the medical device data back to a standard common denominator, that is, HL7 v3 RIM from which all the other medical domains under HL7 v3 are derived. Hence, once the medical device data are obtained in the RMIM format, it can easily be transformed into HL7-based standard interfaces through XML transformations because these interfaces all have their building blocks from the same RIM. To demonstrate this, we provide the mappings from the developed RMIM to some of the widely used HL7 v3-based standard interfaces.

Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract—Transform—Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM.Purpose: In this study, our aim is to develop a modular but coordinated transformation approach in order to separate semantic and technical steps of transformation processes, which do not have a strict separation in traditional ETL approaches. Such an approach would discretize the operations to extract data from source electronic health record systems, alignment of the source, and target models on the semantic level and the operations to populate target common data repositories.Approach: In order to separate the activities that are required to transform heterogeneous data sources to a target CDM, we introduce a semantic transformation approach composed of three steps: (1) transformation of source datasets to Resource Description Framework (RDF) format, (2) application of semantic conversion rules to get the data as instances of ontological model of the target CDM, and (3) population of repositories, which comply with the specifications of the CDM, by processing the RDF instances from step 2. The proposed approach has been implemented on real healthcare settings where Observational Medical Outcomes Partnership (OMOP) CDM has been chosen as the common data model and a comprehensive comparative analysis between the native and transformed data has been conducted.Results: Health records of ~1 million patients have been successfully transformed to an OMOP CDM based database from the source database. Descriptive statistics obtained from the source and target databases present analogous and consistent results.Discussion and Conclusion: Our method goes beyond the traditional ETL approaches by being more declarative and rigorous. Declarative because the use of RDF based mapping rules makes each mapping more transparent and understandable to humans while retaining logic-based computability. Rigorous because the mappings would be based on computer readable semantics which are amenable to validation through logic-based inference methods.