Enabling patient adherence via personalised, just-in time adaptive interventions in ADLIFE architecture

Chronic diseases introduce challenges for the patients to continuously be involved in their care activities and manage the changing requirements of their disease. Patient empowerment activities are a critical component to assist patients in their long-term care journey. In the ADLIFE project (H2020, SC1-DTH-11-2019, 875209), an integrated care planning approach is used where patients are assigned various care plan activities by multidisciplinary care teams. To increase patients’ adherence to the care plan, a continuous behavioral monitoring architecture is developed for delivering digital personalised, just-in time adaptive interventions

From Raw Data to FAIR Data: The FAIRification Workflow for Health Research

BackgroundFAIR (findability, accessibility, interoperability, and reusability) guidingprinciples seek the reuse of data and other digital research input, output, and objects(algorithms, tools, and workflows that led to that data) making themfindable, accessible,interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governedinitiative-defined a seven-step FAIRificationprocessfocusingondata,butalsoindicatingtherequired work for metadata. This FAIRification process aims at addressing the translation ofraw datasets into FAIR datasets in a general way, without considering specific requirementsand challenges that may arise when dealing with some particular types of data.This work was performed in the scope of FAIR4Healthproject. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovationprogramme under grant agreement number 824666

The design of a mobile platform providing personalized assistance to older multimorbid patients with mild dementia or mild cognitive impairment (MCI)

Management of multiple chronic conditions introduces demanding challenges for patients. This situation becomes more complex when multimorbidity is associated with dementia. In this paper, we present the design of a mobile Patient Empowerment Platform that enables older multimorbid patients with mild dementia or mild cognitive impairment (MCI) to easily follow their complex care plans and increase their adherence. We focus on the presentation of the human-centered design process that we have followed with the involvement of patients, informal caregivers, and healthcare professionals via the clinical pilot sites of the CAREPATH project. We elaborate the design challenges we have faced and present the iterative mock-ups that have been created in cooperation with end users to address these challenges and the final PEP design

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

This article belongs to the Special Issue Addressing the Growing Burden of Chronic Diseases and Multimorbidity: Characterization and InterventionsThe current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164].Peer reviewe

Protocol for creating a single, holistic and digitally implementable consensus clinical guideline for multiple multi-morbid conditions

Delivery of future healthcare information systems requires systems to support patients with multi-morbidity. Current approaches to computer interoperable guidelines typically consider only a single clinical guideline for a single condition. There is a need to establish a robust protocolized approach to the development of holistic consensus computer interoperable guidelines in the context of multi-morbidity. The presence of mild cognitive impairment (MCI) and dementia adds an additional challenge to the delivery of effective digital health solutions. CAREPATH proposes an ICT-based solution for the optimization of clinical practice in the treatment and management of multi-morbid older adults with mild cognitive impairment or mild dementia. In this manuscript, we present an evidence-based protocol for the development of a single computer interoperable holistic guideline for a collection of multi-morbid conditions. To the best of our knowledge, this is the first published protocol for the production of a consensus interoperable clinical guideline for people with multi-morbidity, with special focus on older adults with MCI or mild dementia. This addresses a still unmet need for such processes which are expected to play a central role for future integrated healthcare information systems

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’.Peer reviewe

Interoperability Profiles for Disaster Management and Maritime Surveillance

Nowadays, many different Command and Control (C2) Systems and Sensor order to manage disasters effectively and achieve powerful surveillance, it is essential for C2 and Sensor Systems to cooperate and exchange timely available, reliable and intelligible information. Although globally accepted standards are used commonly in C2 and Sensor domains, there is no single specification of using these standards together for cooperation of disparate systems, which creates a crucial interoperability challenge. To address this challenge, profiling is a practical approach to achieve how profiling approach can address the interoperability challenge among C2 and Sensor Systems, and present 13 identified profiles to be used by organizations with their C2 and Sensor Systems in disaster management and maritime surveillance activities

Ultrasound-guided injection for MR arthrography of the hip: comparison of two different techniques

The purpose of this study was to prospectively evaluate the two different ultrasound-guided injection techniques for MR arthrography of the hip