Knowledge Guided Integration of Structured and Unstructured Data in Health Decision Process

Data in the health domain is continuously increasing. It is collected from several sources, has several formats and is characterized by its sensibility (protection of personal health data). These characteristics make the management and the expert interaction with the collected data, in order to facilitate decision-making in Health Information Systems (HIS) a challenging field. In this paper, we propose a Knowledge guided integration of structured and unstructured data for health decision process. The knowledge is represented by domain ontology, which allows the integration of structured and unstructured data, stored in NoSQL format. Our motivation is to combine the confirmed advantages of ontologies and NoSQL databases both in data integration and decision aided processes. The proposed ontology has been implemented and evaluated using quality metrics. The approach was evaluated and results show response time optimization, compared with traditional approaches, and improvement of data relevance

Improving Health Information Exchange by Combining Interoperable Resources and Digital Collection Integration Tools

Health information exchange plays a key role in any kind of healthcare system. An important barrier in this exchange is the lack of mechanisms and tools able to fully integrate information models that underlie existing healthcare systems. The current paper presents an approach to gathering multiple sources of clinical data by taking advantage of the HL7 FHIR (Fast Healthcare Interoperability Resources) format. The use of this standard format together with the Clavy tool enables a powerful approach to managing digital health collections that can easily be exchanged in different healthcare domains. In this sense, several content items for healthcare training, based on e-learning standards, have been generated from a clinical dataset that combines FHIR resources and DICOM images. Such a generation process shows the capability of the approach presented to coping with the exchange of health information based on multiple multimedia formats and controlled medical vocabularies

Towards the integration of enterprise software: The business manufacturing intelligence

Nowadays, the Information Communication Technology has pervaded literally the companies. In the company circulates an huge amount of information but too much information doesn’t provide any added value. The overload of information exceeds individual processing capacity and slowdowns decision making operations. We must transform the enormous quantity of information in useful knowledge taking in consideration that information becomes obsolete quickly in condition of dynamic market. Companies process this information by specific software for managing, efficiently and effectively, the business processes. In this paper we analyse the myriad of acronyms of software that is used in enterprises with the changes that occurred over the time, from production to decision making until to convergence in an intelligent modular enterprise software, that we named Business Manufacturing Intelligence (BMI), that will manage and support the enterprise in the futurebusiness manufacturing intelligence, enterprise resource planning; business intelligence; management software; automation software; decision making software

Can policy making be evidence-based?

Ministers are always calling for more evidence-based interventions. Do they apply the same criterion to their own work of making policy? Perhaps surprisingly, policy making is not an evidence-free zone. However, it is important to understand the ways in which policy makers in different situations will use information differently, count different kinds of information as evidence, and so exercise different styles of judgment