Ontological modeling of electronic health information exchange

Abstract

The ontology was populated with data from a regional health system and the flows were measured. Individual instance's properties were inferred from their class associations as determined by their data and object property rules. It was also possible to visualize interoperability activity for regional analysis and planning purposes. A property called Impact was created from the total number of patients or clients that a health entity in the region served in a year, and the total number of health service providers or organizations with whom it exchanged information in support of clinical decision-making, diagnosis or treatment. Identifying providers with a high Impact but low Interoperability score could assist planners and policy-makers to optimize technology investments intended to electronically share patient information across the continuum of care. Finally, we demonstrated how linked ontologies were used to identify logical inconsistencies in self-reported data for the study.

Improving quality and use of routine health information system data in low- and middle-income countries: A scoping review

“…Two papers reported cross-sectional studies [29,45]. A majority of the articles on the use of data were case studies [25,43,48,52,55,56,58], two had used a mixed-methods approach [36,54], three were qualitative studies [37,50,53], and two claimed evaluation designs that were not described [30,36]. The target population for the data quality interventions was mainly health facilities at the district level and or their staff, including health care providers and data generators [29,31,33,35,[38][39][40][41][42]44].…”

“…The target population for data use interventions was similar to the data quality intervention studies. They focused on health facilities and their staff [48,50,53,57,58], or the health system in general, including its health management information system [36,55,56], or the management team, primarily at the district level [30,37,54], or a combination of the health facility and the district management team [25]. One study considered community health workers and the community health information system [25] (S1A and S1B Table).…”

The Emergence of Openness in Open-Source Projects: The Case of OpenEhR

“…Since they define and classify semantic knowledge, such systems can contribute to the interoperable exchange of information by developing compatible model representations of information as abstracted concepts, which can later be exchanged and interpreted by machines (Soguero-Ruiz et al, 2013), even if the machines follow different standards (Berges et al, 2012). Following from increased interoperability, ontology-based information systems hold promising expectations to be more amenable to complex queries (González-Beltrán et al, 2012), along the patient's medical history (McMurray et al, 2015), and become less susceptible to change (Wang et al, 2014). In other words, descriptions that need to hold clinical meaning and are not fixed to terminological descriptions of pathologies, which is especially important in the health domain where the changing interpretation of concepts is commonplace (Mol 2002).…”

The end of scientific articles as we know them?

“…There might, for example, be an ontology for clinical interventions to promote smoking cessation, or for environmental interventions to promote healthy eating, or counseling interventions to increase physical activity levels. Ontologies have already begun to be developed in public health science (e.g., [1,2]). Such ontologies could be constructed using a standard format allowing them to be linked to create super ontologies.…”