Dataset Information

Assessing the adequacy of the HL7/LOINC Document Ontology Role axis.

ABSTRACT: The healthcare landscape is changing, driven by innovative care models and the emergence of new roles that are inter-professional in nature. Currently, the HL7/LOINC Document Ontology (DO) aids the use and exchange of clinical documents using a multi-axis structure of document attributes for Kind of Document, Setting, Role, Subject Matter Domain, and Type of Service. In this study, the adequacy of the Role axis for representing the type of author documenting care was assessed. Experts used a master list of 220 values created from seven resources and established mapping guidelines. Baseline certification, licensure, and didactic training were identified as key parameters that define roles and hence often need to be pre-coordinated. DO was inadequate in representing 82% of roles, and this gap was primarily due to lack of granularity in DO. Next steps include refinement of the proposed schema for the Role axis and dissemination within the larger standards community.

SUBMITTER: Rajamani S

PROVIDER: S-EPMC5901117 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Assessing the adequacy of the HL7/LOINC Document Ontology Role axis.

Rajamani Sripriya S Chen Elizabeth S ES Akre Mari E ME Wang Yan Y Melton Genevieve B GB

Journal of the American Medical Informatics Association : JAMIA 20141028 3

The healthcare landscape is changing, driven by innovative care models and the emergence of new roles that are inter-professional in nature. Currently, the HL7/LOINC Document Ontology (DO) aids the use and exchange of clinical documents using a multi-axis structure of document attributes for Kind of Document, Setting, Role, Subject Matter Domain, and Type of Service. In this study, the adequacy of the Role axis for representing the type of author documenting care was assessed. Experts used a mas ...[more]

PMID: 25352569

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Project description:BackgroundMobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.MethodsThis paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.ResultsThis realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients' wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO .ConclusionsThe resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

Dataset Information

Assessing the adequacy of the HL7/LOINC Document Ontology Role axis.

Publications

Assessing the adequacy of the HL7/LOINC Document Ontology Role axis.

Similar Datasets

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