Objectives: Presentation of an overview of the reasons why data integration initiatives should be seen as enablers for effective decision support in data-intensive healthcare settings. Methods: Typical challenges rising from the information requirements of clinical decision support systems are highlighted. We then propose a methodological solution where several heterogeneous data sources are integrated by the means of a common data model on top of which the DSS is built. Results: We report on two successful case studies based on the DSSs developed in the context of the MobiGuide and Mosaic projects, funded by the European Union in the Seventh Framework Program. The MobiGuide patient guidance system has been successfully validated during a recent pilot study involving 30 patients (10 with atrial fibrillation and 20 with gestational diabetes), while Mosaic is currently undergoing a validation phase involving 1000 type 2 Diabetes patients. Conclusions: In the era of big data, effective data integration strategies are an essential need for medical informatics solutions and even more for those intended to support decision processes. Building generic DSSs based on a stable (but easily extensible) data model, specifically designed to meet the information requirements of DSSs and analytics, has proven to be a successful solution in the two presented use cases.

• Klíčová slova
• projekt MOSAIC, projekt MobiGuide,
• MeSH
• diabetes mellitus 2. typu diagnóza   terapie   MeSH
• fibrilace síní diagnóza   terapie   MeSH
• Health Level Seven MeSH
• informace pro uživatele zdravotní péče * MeSH
• lidé MeSH
• rozhodování pomocí počítače MeSH
• systémová integrace MeSH
• systémy pro podporu klinického rozhodování * MeSH
• teoretické modely MeSH
• zapojení pacienta MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

• MeSH
• big data * MeSH
• elektronické zdravotní záznamy organizace a řízení   MeSH
• kardiovaskulární rehabilitace metody   MeSH
• systémová integrace MeSH
• Publikační typ
• rozhovory MeSH
• Geografické názvy
• Izrael MeSH

• MeSH
• dokumentace MeSH
• dostupnost zdravotnických služeb MeSH
• důvěrnost informací MeSH
• ekonomika a organizace zdravotní péče MeSH
• integrované poskytování zdravotní péče MeSH
• osobní údaje MeSH
• veřejné zdravotnictví - informatika MeSH
• zdravotní politika MeSH
• zdravotnické informační systémy MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• ekonomie, ekonomika, ekonomika zdravotnictví
• NLK Publikační typ
• studie

Objectives: A significant portion of care related to cardiorespiratory diseases is provided at home, usually but not exclusively, after the discharge of a patient from hospital. It is the purpose of the present study to present the technical means which we have developed, in order to support the adaptation of the continuity of care of cardiorespiratory diseases at home. Methods: We have developed an integrated system that includes: first, a prototype laptop-based portable monitoring system that comprises low-cost commercially available components, which enable the periodical or continuous monitoring of vital signs at home; second, software supporting medical decision-making related to tachycardia and ventricular fibrillation, as well as fuzzy-rules-based software supporting home-ventilation optimization; third, a typical continuity of care record (CCR) adapted to support also the creation of a homecare plan; and finally, a prototype ontology, based upon the HL7 clinical document architecture (CDA), serving as basis for the development of semantically annotated web services that allow for the exchange and retrieval of homecare information. Results: The flexible design and the adaptable data-exchange mechanism of the developed system result in a useful and standard-compliant tool, for cardiorespiratory disease-related homecare. Conclusions: The ongoing laboratory testing of the system shows that it is able to contribute to an effective and low-cost package solution, supporting patient supervision and treatment. Furthermore, semantic web technologies prove to be the perfect solution for both the conceptualization of a continuity of care data exchange procedure and for the integration of the structured medical data.

• MeSH
• informační systémy normy   MeSH
• internet MeSH
• kardiovaskulární nemoci MeSH
• kontinuita péče o pacienty MeSH
• lidé MeSH
• sémantika MeSH
• služby domácí péče MeSH
• systémová integrace MeSH
• telemetrie metody   přístrojové vybavení   MeSH
• ukládání a vyhledávání informací metody   MeSH
• zajištění kvality zdravotní péče metody   MeSH
• Check Tag
• lidé MeSH

• MeSH
• hodnocení rizik MeSH
• nejistota MeSH
• sběr dat normy   MeSH
• vystavení vlivu životního prostředí MeSH

• Konspekt
• Životní prostředí a jeho ochrana
• NLK Obory
• environmentální vědy
• NLK Publikační typ
• publikace WHO

• MeSH
• analýza dat MeSH
• Publikační typ
• kongresy MeSH
• sborníky MeSH

• Konspekt
• Biologické vědy
• NLK Obory
• lékařská informatika

• Klíčová slova
• poradenská centra pro vysokoškoláky, životní spokojenost, koncept sebeúčinnosti,
• MeSH
• lidé MeSH
• neuroticismus MeSH
• neurotické poruchy MeSH
• psychický stres * MeSH
• psychologie MeSH
• sběr dat MeSH
• studenti MeSH
• vyučování MeSH
• Check Tag
• lidé MeSH

• MeSH
• kooperační chování MeSH
• lékařská praxe založená na důkazech MeSH
• sběr dat MeSH
• telemedicína MeSH
• zdravotnické informační systémy MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• lékařská informatika
• NLK Publikační typ
• publikace WHO
• elektronické knihy

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

• MeSH
• algoritmy MeSH
• big data * MeSH
• individualizovaná medicína metody   MeSH
• lidé MeSH
• poskytování zdravotní péče MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH