Research Observatory: Observing knowledge organization trajectories in healthcare (Online)

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Datum/Tijd
Date(s) - 30/06/2021
18:30 - 19:45

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Home care nursing information behavior: expansion of a knowledge base in a time of pandemic
By Richard P. Smiraglia, Edmund Pajarillo, Elizabeth Milonas & Sergey Zherebchevsky

Home care nursing has become a frontline critical role in pandemic health care. In 2020 as the pandemic’s proportions were becoming apparent we turned to research on home care nurses that had yielded a theory called the “nub of Nursing Information Behavior (NIB).” A “Core Taxonomy for Nursing Information Behavior, or CT-NIB was published in June 2020 and subsequently was enhanced with mappings to the NANDA-International Nursing Diagnoses and Classification (NANDA-I) (https://knoworg.org/a-core-taxonomy-of-nursinginformation-behavior-ct-nib-version-1-1/). As the pandemic evolved ethnographic techniques were employed to discover ways in which the knowledge base of NIB was affected over time. A collection of videos was compiled, transcripts were generated and subjected first to co-word analysis and then the narrative analysis. Co-word analysis revealed larges core regions in the vocabulary of active home care nurses: the community of home health care people, hospital nurs[ing] service and care taking, with pointers to the front line of home care for COVID-19 patients. Narrative analysis new contours for the knowledge base. The vocabulary of the pandemic itself becomes part of the knowledge base of the home care nurse together with an emotional layer beyond the core vocabulary of NIB that reveals the contours of the social impact of the pandemic.

Making healthcare data FAIR data: the ontologies-data models-instances conundrum
By Ronald Cornet

Exchange of healthcare data is crucial for providing adequate care, for monitoring the quality of provided care, and to improve healthcare by research on observational data. For these, healthcare data need to be of good quality, and as much as possible structured and standardized according to agreed-upon data models and ontologies. Increasingly, healthcare data is represented using ontologies such as SNOMED CT. This is of great value in well-delineated areas of healthcare records, such as diagnosis or procedures. However, the benefit of reasoning by using the OWL language is underutilized, and essential for application of ontologies on the broader scale of healthcare data. This talk will briefly summarize the content and structure of SNOMED CT, describe its current use in healthcare records, and concludes with the further steps needed to establish truly interoperable healthcare data, to pave the way towards FAIR healthcare data.
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