ArticleBMC medical informatics and decision making2019
A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review.Journal of medical Internet research · 2024Pooled it
- Content and quality of physical activity ontologies: a systematic review.The international journal of behavioral nutrition and physical activity · 2023Pooled it
- Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review.JMIR diabetes · 2026Review
- Article
- A vital physiological parameter mobile monitoring system for elderly patients in remote areas.BMC medical informatics and decision making · 2026Article
- Development and evaluation of the COntextualised and Personalised Physical activity and Exercise Recommendations (COPPER) Ontology.The international journal of behavioral nutrition and physical activity · 2025Article
- Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Scoping Review.Journal of medical Internet research · 2025Article
- State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review.JMIR medical informatics · 2024Article
- Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability.JMIR medical informatics · 2024Review
- Regulatory Standards and Guidance for the Use of Health Apps for Self-Management in Sub-Saharan Africa: Scoping Review.Journal of medical Internet research · 2024Article
- Evaluating the Feasibility and Acceptance of a Mobile Clinical Decision Support System in a Resource-Limited Country: Exploratory Study.JMIR formative research · 2023Article
- Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review.JMIR medical informatics · 2023Article
- Assessment of Stakeholder Perceptions and Attitudes Toward Health Data Governance Principles in Botswana: Web-Based Survey.JMIR formative research · 2023Article
- Ontologies Applied in Clinical Decision Support System Rules: Systematic Review.JMIR medical informatics · 2023Review
- Review
- [Ontologies Applied in Clinical Decision Support Systems for Diabetes].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023Review
- Telehealth System Based on the Ontology Design of a Diabetes Management Pathway Model in China: Development and Usability Study.JMIR medical informatics · 2022Article
- Process quality of type 2 diabetes mellitus care and association with patient perceived attributes of family doctor service in urban general practices, Beijing, China.BMC primary care · 2022Article
- Acceptance of the District Health Information System Version 2 Platform for Malaria Case-Based Surveillance By Health Care Workers in Botswana: Web-Based Survey.JMIR formative research · 2022Article
- Development of a conceptual framework for linking mHealth applications to eRecord systems in Botswana.BMC health services research · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.