A novel tool for standardizing clinical data in a realism-based common data model

Standardizing clinical information in a common data model is important for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a clinical data model accurately reflects the reality of the clinical situation it describes. To this end, the Open Biomedical Ontologies Foundry provides a set of ontologies that conform to the principles of realism and can be used to create a realism-based clinical data model. However, the challenge of programmatically defining such a model and loading data from disparate sources into the model has not been addressed by pre-existing software solutions. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that works in conjunction with data aggregation software to transform source-specific RDF data into a source-independent, realism-based data model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable RDF data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard realism-based clinical data model.

Nursing Process Addressing the Nursing Focus “Hallucination”: A Scoping Review

Although hallucinations are prevalent in psychiatric disorders, such as psychosis or dementia, no studies were to be found in literature about the nursing process addressing the focus “Hallucination”. This literature review, which is integrated with a scoping study framework, was performed to determine a clinical data model addressing the focus “Hallucination”. PRISMA checklist for scoping reviews was followed. From the total of 328 papers found, 32 were selected. The findings of this review were summarized according to the nursing process addressing the focus “Hallucination”. These findings led to determine a clinical data model addressing the focus “Hallucination”, comprising the elements of the nursing process. This clinical data model may contribute toward improving nursing decision-making and nursing care quality in relation to a client suffering from hallucination, as well as contribute toward producing more reliable nursing-sensitive indicators.

A nursing clinical data model for neuromuscular processes: content analysis of the Portuguese nursing customization

This study targets the development of a nursing clinical data model for neuromuscular processes. To achieve this purpose, content analysis based on Bardin’s perspective was performed on the Portuguese nursing local customizations regarding neuromuscular processes, with the International Classification for Nursing Practice concepts and the ISO 18104:2014 used as encoding rules. From analysis of the data, a total of 1766 diagnoses were related to neuromuscular processes. After application of exclusion criteria, a corpus with a total of 900 diagnoses was subjected to content analysis. After application of the encoding rules, a total of 81 context units were obtained, and through an inductive approach, were defined into three categories: clinical findings (e.g. aphasia); negative judgment diagnoses (e.g. impaired communication); transition properties (e.g. preparation and knowledge). These interpretations were validated by experts in the field. This study not only demonstrates the need to standardize data, but also the importance of neuromuscular processes in nursing practice. We hope this study will guide the definition of a nursing clinical data model that will help in increasing complexity in the level of care provided with high impact in the patient’s quality of life.

Linking a Medical Vocabulary to a Clinical Data Model using Abstract Syntax Notation 1

We have created a clinical data model using Abstract Syntax Notation 1 (ASN.l). The clinical model is constructed from a small number of simple data types that are built into data structures of progressively greater complexity. Important intermediate types include Attributes, Observations, and Events. The highest level elements in the model are messages that are used for inter-process communication within a clinical information system. Vocabulary is incorporated into the model using BaseCoded, a primitive data type that allows vocabulary concepts and semantic relationships to be referenced using standard ASN.l notation. ASN.l subtyping language was useful in preventing unbounded proliferation of object classes in the model, and in general, ASN.l was found to be a flexible and robust notation for representing a model of clinical information.