Razumijevanje osnova privatnosti u zdravstvenoj zaštiti

Achieving and maintaining patient privacy and data protection are inevitable in providing trustworthy healthcare, but often the topics that have not been given enough attention. Understanding privacy basics is crucial for empowering citizens in care, e. g. patients and protégés as well as healthcare workers while providing healthcare. This work covers the following areas and topics of the privacy basics in the context of healthcare: privacy as a human right; short history of privacy; personal privacy protection; privacy and dignity; privacy vs. confidentiality; privacy and availability; cybersecurity vs. privacy; cybersecurity including “CIA triad” and patient safety; privacy in healthcare including legal aspects of data protection. Although this work is suitable for readers with no previous knowledge in privacy, it is useful to relate to everyday-life examples from private and professional surroundings when learning about privacy in healthcare, from both patient and healthcare worker perspectives if applicable. Privacy protection, patient data confidentiality and overall cybersecurity are of huge importance in achieving reliable, trustworthy, safe, and secure environment for both patients and healthcare workers as well as the data they process in any applicable sense.Ostvarivanje i očuvanje privatnosti pacijenata i zaštite podataka neizbježni su u pružanju pouzdane zdravstvene skrbi, ali često i teme kojima se ne pridaje dovoljno pažnje. Razumijevanje osnova privatnosti ključno je za osnaživanje građana u skrbi, npr. pacijenata i štićenika kao i zdravstvenih radnika pri pružanju zdravstvene zaštite. Ovaj rad pokriva sljedeća područja i teme osnova privatnosti u kontekstu zdravstvene zaštite: privatnost kao ljudsko pravo; kratka povijest privatnosti; zaštita osobne privatnosti; privatnost i dostojanstvo; privatnost i povjerljivost; privatnost i raspoloživost; kibernetička sigurnost nasuprot privatnosti; kibernetička sigurnost uključujući „CIA trojku“ i sigurnost pacijenata; privatnost u zdravstvu uključujući pravne aspekte zaštite podataka. Iako je ovaj rad prikladan za čitatelje bez predznanja o privatnosti, korisno je pozvati se na primjere iz svakodnevnog života iz privatnog i profesionalnog okruženja kada se uči o privatnosti u zdravstvu, kako iz perspektive pacijenata tako i iz perspektive zdravstvenog radnika ako je primjenjivo. Zaštita privatnosti, povjerljivost podataka o pacijentima i cjelokupna kibernetička sigurnost od velike su važnosti u postizanju pouzdanog, vjerodostojnog, sigurnog i štićenog okruženja za pacijente i zdravstvene radnike, kao i podatke koje obrađuju u bilo kojem primjenjivom smislu

A new trend for knowledge-based decision support systems design

Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Computational Logic for Biomedicine and Neurosciences

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis

Process of designing robust, dependable, safe and secure software for medical devices: Point of care testing device as a case study

This article has been made available through the Brunel Open Access Publishing Fund.Copyright © 2013 Sivanesan Tulasidas et al. This paper presents a holistic methodology for the design of medical device software, which encompasses of a new way of eliciting requirements, system design process, security design guideline, cloud architecture design, combinatorial testing process and agile project management. The paper uses point of care diagnostics as a case study where the software and hardware must be robust, reliable to provide accurate diagnosis of diseases. As software and software intensive systems are becoming increasingly complex, the impact of failures can lead to significant property damage, or damage to the environment. Within the medical diagnostic device software domain such failures can result in misdiagnosis leading to clinical complications and in some cases death. Software faults can arise due to the interaction among the software, the hardware, third party software and the operating environment. Unanticipated environmental changes and latent coding errors lead to operation faults despite of the fact that usually a significant effort has been expended in the design, verification and validation of the software system. It is becoming increasingly more apparent that one needs to adopt different approaches, which will guarantee that a complex software system meets all safety, security, and reliability requirements, in addition to complying with standards such as IEC 62304. There are many initiatives taken to develop safety and security critical systems, at different development phases and in different contexts, ranging from infrastructure design to device design. Different approaches are implemented to design error free software for safety critical systems. By adopting the strategies and processes presented in this paper one can overcome the challenges in developing error free software for medical devices (or safety critical systems).Brunel Open Access Publishing Fund

Employing AI Applications to Authenticate People through Neural Networks

Artificial intelligence is used to develop techniques for verifying people by sensory factors. This article aims to design a robot to verify the entry of the authorized persons to the firms. This paper uses neural networks with artificial intelligence applications to authenticate people, while the program is based on testing four factors: the face, eye, voice, and handprint. The AI application depends on a mathematical algorithm to test the authority of staff; meanwhile, neural networks analyse and examine the visual systems that connect imaging devices (camera) with a computer. Moreover, this is done through the huge amount of data in a smart computer database that can be updated, with speed and objectivity, through the Internet to reach accurate results. The results indicate that the model designed for artificial intelligence has economic feasibility; in addition to that, it can help detect diseases that can affect employees by multiple parametric methods of verification