Exploring Greek Population Attitudes Towards Using Health-Related Websites for Managing Their Health.

The last few years the Internet has evolved into a prominent information source for many people worldwide. Latest research has shown that an ever increasing number of citizens and patients go online in order to access health information and seek support in managing their health, including understanding their condition, adopting life-saving lifestyle adjustments and keeping up with treatment or aftercare guidelines. Due to this rise on the demand of online health information, health-related sites have increased substantially, with each one of them striving to maintain the most comprehensive and reliable source of health and medical information on the Internet. This paper presents a survey conducted among Greek population aiming at exploring participants general attitudes towards using the Internet to access health information as well as their views regarding a specific Greek health-related website, namely Iatronet. To this end, an online Greek version of eHealth Impact Questionnaire has been used which was developed using RedCAP platform.

Greek Tweets on Long COVID: Topic Modelling Following Sentiment Analysis and ChatGPT Interpretation.

Long COVID is a disease that makes it hard for patients to get an official diagnosis while it impacts their quality of life. Many people are turning to social networks such as Facebook, WhatsApp, Twitter (now X) to express their opinions and feelings regarding Long COVID. In this paper, positive (or neutral) and negative text messages in the Greek language, posted on the Twitter platform in 2022, regarding Long COVID are analyzed and popular discussion topics are extracted. Analysis revealed that when topic modelling follows sentiment analysis more coherent topics are created. Furthermore, ChatGPT is used to assign a label to each topic that, in turn, is assessed by a human expert.

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.

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.

Towards a mLearning training solution to the adoption of a CPOE system.

Computerized Physician Order Entry (CPOE) has been introduced as a solution that can fundamentally change the way healthcare is provided, affecting all types of healthcare stakeholders and improving healthcare decisions, patient outcomes, patient safety and efficiency. However, a relatively small proportion of healthcare organizations have implemented CPOE systems, due to its technological complexity and to its low acceptance rate by healthcare professionals who largely disregard the value of CPOE in efficient healthcare delivery. An online training facility embedded within a CPOE service may increase the likelihood of its adoption by healthcare professionals as it offers them guidelines on how to perform each task of the CPOE service. In contrast to CPOE, on the other hand, handheld devices and other mobile technologies have showed an increased adoption rate. This paper considers a CPOE service that can be accessed by authorized healthcare professionals through their mobile devices anytime anywhere, and allows embedded training content, which has been developed through a learning management system (LMS) to be presented to the user automatically upon request.

An Android-enabled mobile framework for ensuring quality of life through patient-centric care.

The drive to achieve excellence in healthcare delivery while containing costs, underlies the need for a new generation of applications which facilitate the realization of a patient-centric care model. Under this emerging care model healthcare delivery can be integrated across the continuum of services, from prevention to follow up, and care can be coordinated across all settings. With care moving out into the community, health systems require real-time information to deliver coordinated care to patients. The integration of leading-edge technologies, such as mobile technology, with Personal Health Records (PHRs) can meet this requirement by making comprehensive and unified health information available to authorized users at any point of care or decision making through familiar environments such as Google's Android. This paper presents a framework that provides ubiquitous access to patients' PHRs via Android-enabled mobile devices. Where possible health information access and management is performed in a transparent way, thus enabling healthcare professionals to devote more time on practicing medicine and patients to manage their own health with the least possible intervention. This depends heavily on the context, which is collected by both Android-specific core system services and special purpose software agents with the latter being also responsible for preserving PHR data privacy.

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.

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.

Healthcare system evolution towards SOA: a security perspective.

Healthcare providers often face the challenge of integrating diverse and geographically disparate IT systems to respond to changing requirements and to exploit the capabilities of modern technologies. Hence, systems evolution, through modification and extension of the existing IT infrastructure, becomes a necessity. This paper assumes a healthcare systems evolution towards a service-oriented architecture (SOA) and places emphasis on the development of an appropriate authorization model and mechanism that ensures authorized access to integrated patient information through web service invocations.

A context-aware approach to process-based PHR system security.

Healthcare delivery is a highly complex process involving a broad range of healthcare services, typically performed by a number of geographically distributed and organizationally disparate healthcare providers requiring increased collaboration and coordination of their activities in order to provide shared and integrated care. Under an IT-enabled, patient-centric model, health systems can integrate care delivery across the continuum of services, from prevention to follow-up, and also coordinate care across all settings. In particular, much potential can be realized if cooperation among disparate healthcare organizations is expressed in terms of cross-organizational healthcare processes, where information support is provided by means of PHR systems. This paper assumes a process-oriented PHR system and presents a security framework that addresses the authorization and access control issues arisen in these systems. The proposed framework ensures provision of tight, just-in-time permissions so that authorized users get access to specific objects according to the current context. These permissions are subject to continuous adjustments triggered by the changing context. Thus, the risk of compromising information integrity during task executions is reduced.

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.

Building interoperable health information systems using agent and workflow technologies.

Healthcare is an increasingly collaborative enterprise involving many individuals and organizations that coordinate their efforts toward promoting quality and efficient delivery of healthcare through the use of interoperable healthcare information systems. This paper presents a mediator-based approach for achieving data and service interoperability among disparate and geographically dispersed healthcare information systems. The proposed system architecture enables decoupling of the client applications and the server-side implementations while it ensures security in all transactions. It is a distributed system architecture based on the agent-oriented paradigm for communication and life cycle management while interactions are described according to the workflow metaphor. Thus robustness, high flexibility and fault tolerance are provided in an environment as dynamic and heterogeneous as healthcare.

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.

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.