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.

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.

An automated skills assessment framework for laparoscopic training tasks.

BACKGROUND: Various sensors and methods are used for evaluating trainees' skills in laparoscopic procedures. These methods are usually task-specific and involve high costs or advanced setups. METHODS: In this paper, we propose a novel manoeuver representation feature space (MRFS) constructed by tracking the vanishing points of the edges of the graspers on the video sequence frames, acquired by the standard box trainer camera. This study aims to provide task-agnostic classification of trainees in experts and novices using a single MRFS over two basic laparoscopic tasks. RESULTS: The system achieves an average of 96% correct classification ratio (CCR) when no information on the performed task is available and >98% CCR when the task is known, outperforming a recently proposed video-based technique by >13%. CONCLUSIONS: Robustness, extensibility and accurate task-agnostic classification between novices and experts is achieved by utilizing advanced computer vision techniques and derived features from a novel MRFS.

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.

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.

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.

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.

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.

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.

Context-aware access control for pervasive access to process-based healthcare systems.

Healthcare is an increasingly collaborative enterprise involving a broad range of healthcare services provided by many individuals and organizations. Grid technology has been widely recognized as a means for integrating disparate computing resources in the healthcare field. Moreover, Grid portal applications can be developed on a wireless and mobile infrastructure to execute healthcare processes which, in turn, can provide remote access to Grid database services. Such an environment provides ubiquitous and pervasive access to integrated healthcare services at the point of care, thus improving healthcare quality. In such environments, the ability to provide an effective access control mechanism that meets the requirement of the least privilege principle is essential. Adherence to the least privilege principle requires continuous adjustments of user permissions in order to adapt to the current situation. This paper presents a context-aware access control mechanism for HDGPortal, a Grid portal application which provides access to workflow-based healthcare processes using wireless Personal Digital Assistants. The proposed mechanism builds upon and enhances security mechanisms provided by the Grid Security Infrastructure. It provides 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.