Investigation Interoperability Problems in Pharmacy Automation: A Case Study in Saudi Arabia

The aim of this case study is to investigate the nature of interoperability problems in hospital systems automation. One of the advanced healthcare providers in Saudi Arabia is the host of the study. The interaction between the pharmacy system and automated medication dispensing cabinets is the focus of the case system. The research method is a detailed case study where multiple data collection methods are used. The modelling of the processes of inpatient pharmacy systems is presented using Business Process Model Notation. The data collected is analysed to study the different interoperability problems. This paper presents a framework that classifies health informatics interoperability implementation problems into technical, semantic, organisational levels. The detailed study of the interoperability problems in this case illustrates the challenges to the adoption of health information system automation which could help other healthcare organisations in their system automation projects

Foundation for the Electronic Health Record: An ontological analysis of the HL7 Reference Information Model

Despite the recent advances in information and communication technology that have increased our ability to store and circulate information, the task remains of ensuring that the right sorts of information reach the right sorts of people. In what follows we defend the thesis that efforts to develop efficient means for sharing information across healthcare systems and organizations would benefit from a careful analysis of human action in healthcare organizations, and that the communication of healthcare information and knowledge needs to rest on a sound ontology of social interaction. We illustrate this thesis in relation to the HL7 RIM, which is one centrally important tool for communication in the healthcare domain

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Report on the EHCR (Deliverable 26.2)

This deliverable is the second for Workpackage 26. The first, submitted after Month 12, summarised the areas of research that the partners had identified as being relevant to the semantic indexing of the EHR. This second one reports progress on the key threads of work identified by the partners during the project to contribute towards semantically interoperable and processable EHRs. This report provides a set of short summaries on key topics that have emerged as important, and to which the partners are able to make strong contributions. Some of these are also being extended via two new EU Framework 6 proposals that include WP26 partners: this is also a measure of the success of this Network of Excellence

Report on the EHCR (Deliverable 26.1)

The challenge of richly interpreting electronic health information, in order to populate EHR instances with suitable terms, to provide decision support in the care of individuals, to identify suitable patients for teaching or clinical trials recruitment, and to mine populations of records for public health or to discover new medical knowledge, all require that the heterogeneous clinical entry instances within EHR repositories can be systematically analysed and interpreted. Achieving this requires the combination and co-operation of many different health informatics tools and technologies, underpinned by shared representations of clinical concepts and inferencing formalisms. Much of this work is at the level of R&D, and is well represented across the Semantic Mining consortium. The challenge of WP26 is to build up a vision of the ways in which these historically independent threads of health informatics research can collaborate, and uncover the research challenges that are needed in order to deliver good demonstrations of semantically indexed and richly analysable EHRs. The partners have begun WP26 by acquiring a better knowledge of each other’s areas of endeavour, and are beginning to steer their research interests towards future areas of collaboration

Speech acts and medical records: The ontological nexus

Despite the recent advances in information and communication technology that have increased our ability to store and circulate information, the task of ensuring that the right sorts of information gets to the right sorts of people remains. We argue that the many efforts underway to develop efficient means for sharing information across healthcare systems and organizations would benefit from a careful analysis of human action in healthcare organizations. This in turn requires that the management of information and knowledge within healthcare organizations be combined with models of resources and processes of patient care that are based on a general ontology of social interaction. The Health Level 7 (HL7) is one of several ANSI-accredited Standards Developing Organizations operating in the healthcare arena. HL7 has advanced a widely used messaging standard that enables healthcare applications to exchange clinical and administrative data in digital form. HL7 focuses on the interface requirements of the entire healthcare system and not exclusively on the requirements of one area of healthcare such as pharmacy, medical devices, imaging or insurance transactions. This has inspired the development of a powerful abstract model of patient care called the Reference Information Model (RIM). The present paper begins with an overview of the core classes of the HL7 (Version 3) RIM and a brief discussion of its “actcentered” view of healthcare. Central to this account is what is called the life cycle of events. A clinical action may progress from defined, through planned and ordered, to executed. These modalities of an action are represented as the mood of the act. We then outline the basis of an ontology of organizations, starting from the theory of speech Acts, and apply this ontology to the HL7 RIM. Special attention is given to the sorts of preconditions that must be satisfied for the successful performance of a speech act and to the sorts of entities to which speech acts give rise (e.g. obligations, claims, commitments, etc.). Finally we draw conclusions for the efficient communication and management of medical information and knowledge within and between healthcare organizations, paying special attention to the role that medical documents play in such organizations

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings