SNA: The Optimal Nodes to Raise Nurses' Infosec Awareness.

Social Network Analysis (SNA) can promote Infosec awareness. A sample of 164 nurses selected the most trusted actors to get Infosec updates. UCINET 6 and NetDraw were used for mapping and PSPP 1.6.2 was used for data analysis. Nurses tend to trust managers, colleagues and IT professionals for retrieving Infosec updates.

Mining Greek Tweets on Long COVID Using Sentiment Analysis and Topic Modeling.

Around 10% to 20% of patients experience Long COVID after recovering from COVID-19. Many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their opinions and feelings regarding Long COVID. In this paper, we analyse text messages in the Greek language posted on the Twitter platform in 2022 to extract popular discussion topics and classify the sentiment of Greek citizens regarding Long COVID. Results highlighted the following discussion topics: Greek-speaking users discuss Long COVID effects and time required to heal, Long COVID effects in specific population groups like children and COVID-19 vaccines. 59% of analysed tweets conveyed a negative sentiment while the rest had positive or neutral sentiment. The analysis shows that public bodies could benefit from systematically mining knowledge from social media to understand public's perception of a new disease and take action.

InfoSec Practices - a Survey Conducted in Greek Hospitals.

Background: Langerhans The information technology is present in every aspect of private, social, and professional sphere and is constantly evolving whilst remaining vulnerable to security alerts and attacks. The healthcare sector contains sensitive information and can be compromised causing even fatal delays to healthcare delivery, loss of reputation of the organizations, and traumatic experiences for patients who might be stigmatized by the disclosure of their medical files. Infosec practices that are applied by nurses can make the difference in defending or compromising healthcare data. Objective: The aim of this research is to investigate the Infosec practices which are applied by nurses who work in Greek hospitals. Correlation between the Infosec practices, the knowledge on Infosec policies, and attitude towards Infosec policies will be further examined. Methods: The KAP model is applied and the HAIS-Q tool consisted of 45 items in five areas of interest was used. A sample of 277 nurses was collected. Confidentiality issues and consent were respected. IBM SPSS 25.0 was used for the statistical analysis. Descriptive analysis (mean, st. dev.) and inferential statistics, ANOVA, and Pearson Corerelations were conducted. The significance level was set up to 0.05. Results: A strong correlation between knowledge on Infosec policies and attitude towards Infosec policies, attitude towards Infosec policies and Infosec practices, and knowledge on Infosec policies and Infosec practices was established. Nurses apply good Infosec practices in three out of five areas of interest, while their practices concerning the report of security violations and their practices related to the Internet usage are average. Overall, nurses' Infosec awareness is good. Conclusion: The findings showed that nurses demonstrate average to good knowledge on Infosec policies, good attitude towards Infosec policies, good to average Infosec practices and good total Infosec awareness.The most vulnerable area is the Internet usage.

The Development and Evaluation of an Anti-Phishing E-Learning Intervention for Nurses.

Interactive games can be included in e-learning and train users to avoid web phishing. A multimodal educational intervention consisted of a serious game was developed in order to train nurses about phishing on Internet and was evaluated by experts and end users. The system was considered acceptable and can be used as an interesting learning resource for self-regulated learning. Future research will focus on evaluating the effectiveness of the educational intervention.

Assessing Internal Consistency of HAIS-Q: A Survey Conducted in Greek Hospitals.

Despite the prevailing perception that powerful software and hardware are adequate solutions to minimize information systems' security breaches, privacy remains at stake. Human factors play an important role in maintaining information security as it is evident that non secure practices applied by employees may increase the vulnerability of the systems and lead to privacy issues. Non secure practices found in literature are related to the use of Internet, the use of emails, password management, information handling and incidence reporting. For this purpose a survey was designed to be conducted in seven hospitals in Greece in order to record non secure practices applied by nursing staff and identify correlation factors. This paper presents the reliability test of the HAIS-Q tool which was applied in order to conduct the survey entitled: Examining the Nurses' non secure IT practices in Greek Hospitals as well as the preliminary results of the study.

Using an Extended Technology Acceptance Model to Evaluate Digital Health Services.

The evaluation of digital health services is concerned with assessing user satisfaction, improving the quality of health services and drawing useful conclusions regarding the factors that affect citizens' acceptance and intention to use digital health services. This paper proposes a model for evaluating a health digital service, that of, the Personal Health Insurance Record (PHIR), delivered by the Greek Organization for the Health Care Provision. The proposed model is based on the Technology Acceptance Model (TAM), enhanced with two additional factors: a) user satisfaction and b) safety-privacy. The analysis of the results highlighted that the intention to use is significantly affected by perceived usefulness, perceived ease of use, user satisfaction and safety-privacy. Parameters such as age and familiarity with the use of e-services also seem to determine the intention to use.

Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

INTRODUCTION: The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the developed countries are due to cardiovascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients. AIM: The aim of this paper is to build and compare classification techniques for cardiovascular diseases. METHODS: The dataset contained 4270 patients and 14 attributes and it is available on the UCI data repository. The prediction is a binary outcome (event and no event). Variables of each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). RESULTS: Different classifiers were tested. The SMOTE technique was used in order to solve the class imbalance. The cross-validation method was used in order to estimate how accurately our predictive models will perform. We evaluate our classifiers by using the following metrics: precision, recall, F1-score, Accuracy, AUC (Area Under Curve). CONCLUSIONS: Based on the resluts, the best scores have the Random Forest and Decision Tree classifiers.

Health Professionals' Perception about Big Data Technology in Greece.

INTRODUCTION: Big data is massive amounts of information that can work wonders. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. AIM: The research aim of this study is to investigate the perceptions of the Health Professionals about the Big Data Technology in Healthcare. METHODS: An empirical study was conducted among 151 health professionals (doctors and nurses) to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. A questionnaire was developed in order to measure the aforementioned dimensions. RESULTS: The survey's population was formed by 151 doctors and nurses who are working at private and public hospitals in Greece. The majority of the population have never heard about Big Data. As a result, most of them were not aware of the format of Big Data. CONCLUSION: Based on the study findings, it can be assumed that the majority of the responders did not have knowledge about the Big Data Technology. It is also important that most of them had never been informed about Big Data. It can be assumed that the Healthcare Sector in Greece is not familiar with Big Data Technology yet. Finally,the current study reveals a rather positive attitude toward the usage of Big Data in the Helathcare domain, although there are some doubts about the implementation of the aforementioned technology in the Greek national healthcare system.

Real-Time Process Analytics in Emergency Healthcare.

Emergency medical systems (EMS) are considered to be amongst the most crucial systems as they involve a variety of activities which are performed from the time of a call to an ambulance service till the time of patient's discharge from the emergency department of a hospital. These activities are closely interrelated so that collaboration and coordination becomes a vital issue for patients and for emergency healthcare service performance. The utilization of standard workflow technology in the context of Service Oriented Architecture can provide an appropriate technological infrastructure for defining and automating EMS processes that span organizational boundaries so that to create and empower collaboration and coordination among the participating organizations. In such systems, the utilization of leading-edge analytics tools can prove important as it can facilitate real-time extraction and visualization of useful insights from the mountains of generated data pertaining to emergency case management. This paper presents a framework which provides healthcare professionals with just-in-time insight within and across emergency healthcare processes by performing real-time analysis on process-related data in order to better support decision making and identify potential critical risks that may affect the provision of emergency care to patients.

A Big Data-driven Model for the Optimization of Healthcare Processes.

Healthcare organizations increasingly navigate a highly volatile, complex environment in which technological advancements and new healthcare delivery business models are the only constants. In their effort to out-perform in this environment, healthcare organizations need to be agile enough in order to become responsive to these increasingly changing conditions. To act with agility, healthcare organizations need to discover new ways to optimize their operations. To this end, they focus on healthcare processes that guide healthcare delivery and on the technologies that support them. Business process management (BPM) and Service-Oriented Architecture (SOA) can provide a flexible, dynamic, cloud-ready infrastructure where business process analytics can be utilized to extract useful insights from mountains of raw data, and make them work in ways beyond the abilities of human brains, or IT systems from just a year ago. This paper presents a framework which provides healthcare professionals gain better insight within and across your business processes. In particular, it performs real-time analysis on process-related data in order reveal areas of potential process improvement.

A framework for privacy-preserving access to next-generation EHRs.

Although personalized medicine is optimizing the discovery, development and application of therapeutic advances, its full impact on patient and population healthcare management has yet to be realized. Electronic health Records (EHRs), integrated with data from other sources, such as social care data, Personal Healthcare Record (PHR) data and genetic information, are envisaged as having a pivotal role in realizing this individualized approach to healthcare. Thus, a new generation of EHRs will emerge which, in addition to supporting healthcare professionals in making well-informed clinical decisions, shows potential for novel discovery of associations between disease and genetic, environmental or process measures. However, a broad range of ethical, legal and technical reasons may hinder the realization of future EHRs due to potential security and privacy breaches. This paper presents a HIPAA-compliant framework that enables privacy-preserving access to next-generation EHRs.

Machine Learning for Knowledge Extraction from PHR Big Data.

Cloud computing, Internet of things (IOT) and NoSQL database technologies can support a new generation of cloud-based PHR services that contain heterogeneous (unstructured, semi-structured and structured) patient data (health, social and lifestyle) from various sources, including automatically transmitted data from Internet connected devices of patient living space (e.g. medical devices connected to patients at home care). The patient data stored in such PHR systems constitute big data whose analysis with the use of appropriate machine learning algorithms is expected to improve diagnosis and treatment accuracy, to cut healthcare costs and, hence, to improve the overall quality and efficiency of healthcare provided. This paper describes a health data analytics engine which uses machine learning algorithms for analyzing cloud based PHR big health data towards knowledge extraction to support better healthcare delivery as regards disease diagnosis and prognosis. This engine comprises of the data preparation, the model generation and the data analysis modules and runs on the cloud taking advantage from the map/reduce paradigm provided by Apache Hadoop.

A Framework for Privacy-preserving Classification of Next-generation PHR data.

Personal Health Records (PHRs), integrated with data from various sources, such as social care data, Electronic Health Record data and genetic information, are envisaged as having a pivotal role in transforming healthcare. These data, lumped under the term 'big data', are usually complex, noisy, heterogeneous, longitudinal and voluminous thus prohibiting their meaningful use by clinicians. Deriving value from these data requires the utilization of innovative data analysis techniques, which, however, may be hindered due to potential security and privacy breaches that may arise from improper release of personal health information. This paper presents a HIPAA-compliant machine learning framework that enables privacy-preserving classification of next-generation PHR data. The predictive models acquired can act as supporting tools to clinical practice by enabling more effective prevention, diagnosis and treatment of new incidents. The proposed framework has a huge potential for complementing medical staff expertise as it outperforms the manual inspection of PHR data while protecting patient privacy.

Patient-Centered e-Health Record over the Cloud.

The purpose of this paper is to introduce the Patient-Centered e-Health (PCEH) conceptual aspects alongside a multidisciplinary project that combines state-of-the-art technologies like cloud computing. The project, by combining several aspects of PCEH, such as: (a) electronic Personal Healthcare Record (e-PHR), (b) homecare telemedicine technologies, (c) e-prescribing, e-referral, e-learning, with advanced technologies like cloud computing and Service Oriented Architecture (SOA), will lead to an innovative integrated e-health platform of many benefits to the society, the economy, the industry, and the research community. To achieve this, a consortium of experts, both from industry (two companies, one hospital and one healthcare organization) and academia (three universities), was set to investigate, analyse, design, build and test the new platform. This paper provides insights to the PCEH concept and to the current stage of the project. In doing so, we aim at increasing the awareness of this important endeavor and sharing the lessons learned so far throughout our work.

Enabling the use of enhanced medical SOPs by an mLearning training solution.

Standard Operating Procedures (SOPs) has been introduced as a way to provide direction, improve communication, reduce training time and improve work consistency. In healthcare, SOPs may be considered as a means that can fundamentally change the way healthcare is provided, affecting all types of healthcare stakeholders and improving healthcare decisions and patient safety. Nowadays, providing ehealth services is a necessity, even though some healthcare organizations are reluctant to fully use them. An online mobile training facility embedded within ehealth services may increase the likelihood of their adoption by healthcare professionals, who feel that, when needed, they are provided the necessary support for performing each task, as handheld devices and other mobile technologies are showing increased adoption rates. This paper presents a mobile service that provides training content on SOPs, that can be embedded in a relevant ehealth service and can be accessed by authorized healthcare professionals where and when needed.

A conceptual security framework for personal health records (PHRs).

Electronic personal health record (PHR) is a citizen-centric information tool that allows citizens to control their personal information. However, an ideal PHR should also allow citizens to connect with their formal and informal caregivers (e.g. a family member, a caregiver) and together manage citizen health and social information. This introduces specific challenges in terms of security since multiple parties make entries and require access to PHR data. Since citizens are typically non-security and non-domain experts is considered impossible to control all this information. To this end, this paper presents a conceptual security framework for the employment of an attribute-based PHR access control policy that is continually updated according to providers' local security policies and individual professionals and citizen sharing preferences.

Android-based access to holistic emergency care record.

This paper is concerned with the development of an Emergency Medical Services (EMS) system which interfaces with a Holistic Emergency Care Record (HECR) that aims at managing emergency care holistically by supporting EMS processes and is accessible by Android-enabled mobile devices.

A sophisticated mechanism for enabling real-time mobile access to PHR data.

Faced with rapid changes, such as growing complexity in care delivery, health systems nowadays fall short in their ability to translate knowledge into practice. Mobile technology holds enormous potential for transforming healthcare delivery systems which currently involve cumbersome processes that slow down care and decrease rather than improve safety. However, the limited computing, energy and information storage capabilities of mobile devices are hampering their ability to support increasingly sophisticated applications required by certain application fields, such as healthcare. This paper is concerned with a framework which provides ubiquitous mobile access to comprehensive health information at any point of care or decision making in a way that efficient utilization of mobile device resources is achieved. To this end, a cloud-based push messaging mechanism is utilized which draws upon and enhances Google Cloud Messaging service.

Video segmentation of moving humans for assistive environments.

In this paper, we present two recently proposed efficient methods for human segmentation from video in indoor environments: the illumination sensitive background method and the self-organizing background subtraction (SOBS) method. Both methods maintain multiple background models. The SOBS method has been modified in this work for gray-scale frames, in order to decrease processing times. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The paper presents the algorithmic implementation and modifications details, while results are also presented for a small number of video sequences.

Using a patient-centered approach for health and social care integration.

The drive in using health and social care resources more effectively has resulted in undertaking various efforts towards better coordination in order to improve patient-centered and personalized care for the individuals. This requires horizontal integration in terms of processes among health and social care organizations existing information systems (ISs) and personal health records (PHRs) in order to enable integrated patient information sharing among all the health and social care staff and individuals involved. Service-oriented and business process management (BPM) technologies are considered most appropriate for achieving such integration especially when is required to change existing processes and to integrate diverse information systems. On these grounds, a patient-centered approach is proposed for redesigning health and social care processes and for integrating diverse ISs and PHRs with the objective to meet holistic care goals.