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.

• Keywords
• projekt MOSAIC, projekt MobiGuide,
• MeSH
• Diabetes Mellitus, Type 2 diagnosis   therapy   MeSH
• Atrial Fibrillation diagnosis   therapy   MeSH
• Health Level Seven MeSH
• Consumer Health Information * MeSH
• Humans MeSH
• Decision Making, Computer-Assisted MeSH
• Systems Integration MeSH
• Decision Support Systems, Clinical * MeSH
• Models, Theoretical MeSH
• Patient Participation MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Big Data * MeSH
• Electronic Health Records organization & administration   MeSH
• Cardiac Rehabilitation methods   MeSH
• Systems Integration MeSH
• Publication type
• Interview MeSH
• Geographicals
• Israel MeSH

• MeSH
• Documentation MeSH
• Health Services Accessibility MeSH
• Confidentiality MeSH
• Health Care Economics and Organizations MeSH
• Delivery of Health Care, Integrated MeSH
• Personally Identifiable Information MeSH
• Public Health Informatics MeSH
• Health Policy MeSH
• Health Information Systems MeSH

• Conspectus
• Veřejné zdraví a hygiena
• NML Fields
• veřejné zdravotnictví
• ekonomie, ekonomika, ekonomika zdravotnictví
• NML Publication type
• 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
• Information Systems standards   MeSH
• Internet MeSH
• Cardiovascular Diseases MeSH
• Continuity of Patient Care MeSH
• Humans MeSH
• Semantics MeSH
• Home Care Services MeSH
• Systems Integration MeSH
• Telemetry methods   instrumentation   MeSH
• Information Storage and Retrieval methods   MeSH
• Quality Assurance, Health Care methods   MeSH
• Check Tag
• Humans MeSH

• MeSH
• Risk Assessment MeSH
• Uncertainty MeSH
• Data Collection standards   MeSH
• Environmental Exposure MeSH

• Conspectus
• Životní prostředí a jeho ochrana
• NML Fields
• environmentální vědy
• NML Publication type
• publikace WHO

• MeSH
• Data Analysis MeSH
• Publication type
• Congress MeSH
• Collected Work MeSH

• Conspectus
• Biologické vědy
• NML Fields
• lékařská informatika

• Keywords
• poradenská centra pro vysokoškoláky, životní spokojenost, koncept sebeúčinnosti,
• MeSH
• Humans MeSH
• Neuroticism MeSH
• Neurotic Disorders MeSH
• Stress, Psychological * MeSH
• Psychology MeSH
• Data Collection MeSH
• Students MeSH
• Teaching MeSH
• Check Tag
• Humans MeSH

• MeSH
• Cooperative Behavior MeSH
• Evidence-Based Practice MeSH
• Data Collection MeSH
• Telemedicine MeSH
• Health Information Systems MeSH

• Conspectus
• Veřejné zdraví a hygiena
• NML Fields
• veřejné zdravotnictví
• lékařská informatika
• NML Publication type
• 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
• Algorithms MeSH
• Big Data * MeSH
• Precision Medicine methods   MeSH
• Humans MeSH
• Delivery of Health Care MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH