Managing information in Health Informatics

Abstract

Only once we agree upon our understanding of what words really mean can we debate whether a concept, represented by those words, is or not well represented significantly in specific application. Conceptual clarity and predicative/impredicative competence are the fundamental components for managing information more effectively in Health Informatics, Healthcare and Medicine applications, while promoting innovation and creativity. Medicine was always the art and science of healing. The science became more and more a mechanistic technology in Healthcare; the art was dropped altogether. But uncertainty-as-problem in the past is slowly morphing into the evaluative concept of uncertainty-as-resource. The key change performance factor is education, distinguishing building on sand from building on rock for Health Informatics! Conceptual clarity, more than instrumental obsession is necessary. In this paper, we present the main concepts of fundamental biomedical enhanced knowledge formalization for Health Informatics and Wellbeing of the future.

Disseminating Research Outputs: The CrowdHEALTH Project

“…The CrowdHEALTH project is tackling a number of current trends in health informatics (35) that may influence health care delivery (36), health care professionals (37) and health sciences education (38)(39)(40)(41). It is important that the target audience is addressed through appropriate means in order to be informed about the progress of the project and to raise their awareness about the project and its deliverable outcomes.…”

The Integrated Holistic Security and Privacy Framework Deployed in CrowdHEALTH Project

“…The first one aims at removing attributes that directly identify an individual, the so called direct identifiers (e.g. names, social security numbers, email addresses, ID card numbers, passport numbers, address, phone numbers) (16)(17)(18)(19)(20) while the second aims in pseudo-anonymizing the indirect identifies, in a way that the individual's privacy is not jeopardized ( Table 2). The anonymization takes place at the source of data to avoid any potential security threats during the transmission of data to the CrowdHEALTH system, and to also avoid identification from information disclosure.…”

“…In suppression, the value or a part of the value of the indirect identifier is replaced by an asterisk. CrowdHEALTH achieves k-anonymity (18) in the datasets that processes. For CrowdHEALTH to achieve k-anonymity, the following steps were performed:…”