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

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

Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In the naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable

Authorization schema for electronic health-care records: for Uganda

This thesis discusses how to design an authorization schema focused on ensuring each patient's data privacy within a hospital information system

Using the dual-level modeling approach to develop applications for pervasive healthcare

Health information technology is the area of IT involving the design, development, creation, use and maintenance of information systems for the healthcare industry. Automated and interoperable healthcare information systems are expected to lower costs, improve efficiency and reduce error, while also providing better consumer care and service. Pervasive Healthcare focuses on the use of new technologies, tools, and services, to help patients play a more active role in the treatment of their conditions. Pervasive Healthcare environments demand a huge amount of information exchange, and specific technologies have been proposed to provide interoperability between the systems that comprise such environments. However, the complexity of these technologies makes it difficult to fully adopt them and to migrate Centered Healthcare Environments to Pervasive Healthcare Environments. Therefore, this paper proposes an approach to develop applications in the Pervasive Healthcare environment, through the use of dual-level modeling based on Archetypes. This approach was demonstrated and evaluated in a controlled experiment that we conducted in the cardiology department of a hospital located in the city of Marilia (São Paulo, Brazil). An application was developed to evaluate this approach, and the results showed that the approach is suitable for facilitating the development of healthcare systems by offering generic and powerful capabilities

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Towards an interoperable healthcare information infrastructure - working from the bottom up

Historically, the healthcare system has not made effective use of information technology. On the face of things, it would seem to provide a natural and richly varied domain in which to target benefit from IT solutions. But history shows that it is one of the most difficult domains in which to bring them to fruition. This paper provides an overview of the changing context and information requirements of healthcare that help to explain these characteristics.First and foremost, the disciplines and professions that healthcare encompasses have immense complexity and diversity to deal with, in structuring knowledge about what medicine and healthcare are, how they function, and what differentiates good practice and good performance. The need to maintain macro-economic stability of the health service, faced with this and many other uncertainties, means that management bottom lines predominate over choices and decisions that have to be made within everyday individual patient services. Individual practice and care, the bedrock of healthcare, is, for this and other reasons, more and more subject to professional and managerial control and regulation.One characteristic of organisations shown to be good at making effective use of IT is their capacity to devolve decisions within the organisation to where they can be best made, for the purpose of meeting their customers' needs. IT should, in this context, contribute as an enabler and not as an enforcer of good information services. The information infrastructure must work effectively, both top down and bottom up, to accommodate these countervailing pressures. This issue is explored in the context of infrastructure to support electronic health records.Because of the diverse and changing requirements of the huge healthcare sector, and the need to sustain health records over many decades, standardised systems must concentrate on doing the easier things well and as simply as possible, while accommodating immense diversity of requirements and practice. The manner in which the healthcare information infrastructure can be formulated and implemented to meet useful practical goals is explored, in the context of two case studies of research in CHIME at UCL and their user communities.Healthcare has severe problems both as a provider of information and as a purchaser of information systems. This has an impact on both its customer and its supplier relationships. Healthcare needs to become a better purchaser, more aware and realistic about what technology can and cannot do and where research is needed. Industry needs a greater awareness of the complexity of the healthcare domain, and the subtle ways in which information is part of the basic contract between healthcare professionals and patients, and the trust and understanding that must exist between them. It is an ideal domain for deeper collaboration between academic institutions and industry

Ontological Principles of Disease Management from Public Health Perspective: a Tuberculosis Case Study

Formal ontological representation of clinical conditions and disease management is a key methodology ensuring that the complex knowledge of disease treatment, control and prevention can be represented, stored and accessed in the most appropriate way to help the medical professionals in their decision making. This is of particular importance for the public health domain where the concern is about the affect of the disease on populations rather than individuals.The existing evidence-based knowledge can best be used by professionals if incorporated into care pathways (formal or informal) which relate the sequence of actions necessary for accurate management of diseases to the progression of the illness and treatment. Therefore, there is a need for an ontological framework to be built around care pathways in order to allow the professionals to access the most relevant information at the time of making a decision. In this paper we will illustrate a Tuberculosis (TB) care pathway, as developed at City University, and show how a formal ontological representation can, in principle, serve the needs of information retrieval around this particular disease

Ontology as Product-Service System: Lessons Learned from GO, BFO and DOLCE

This paper defends a view of the Gene Ontology (GO) and of Basic Formal Ontology (BFO) as examples of what the manufacturing industry calls product-service systems. This means that they are products (the ontologies) bundled with a range of ontology services such as updates, training, help desk, and permanent identifiers. The paper argues that GO and BFO are contrasted in this respect with DOLCE, which approximates more closely to a scientific theory or a scientific publication. The paper provides a detailed overview of ontology services and concludes with a discussion of some implications of the product-service system approach for the understanding of the nature of applied ontology. Ontology developer communities are compared in this respect with developers of scientific theories and of standards (such as W3C). For each of these we can ask: what kinds of products do they develop and what kinds of services do they provide for the users of these products