The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.

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.

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.

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.