Desiderata for discoverability and FAIR adoption of health data hubs.

Background: The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens.

Objective: This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best. practices for new data hubs joining the HRIC ecosystem.

A prospective observational concordance study to evaluate computational model-driven clinical practice guidelines for Type 2 diabetes mellitus.

Background: Clinical Practice Guidelines (CPGs) provide healthcare professionals with performance and decision-making support during the treatment of patients. Sometimes, however, they are poorly implemented. The IDEICDS platform was developed and validated with CPGs for type 2 diabetes mellitus (T2DM).

Health data hubs: an analysis of existing data governance features for research.

Background: Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data are managed by dedicated infrastructures called data hubs is essential to facilitating data sharing and reuse.

Methods: To capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out.

FAIR principles to improve the impact on health research management outcomes.

Background: The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes.

From FAIR4Health Project to 1+MG Initiative: A Spain - Italy Collaboration.

Results of two major projects funded by the European Union are taken into consideration: Fair4Health regarding the possibility of sharing clinical data in various environments applying FAIR principles and 1+Million Genome for the in-depth study of the human genome in Europe. Specifically, the Gaslini hospital plans to move on both areas joining the Hospital on FHIR initiative matured within the fair4health project and also collaborate with other Italian healthcare facilities through the implementation of a Proof of Concept (PoC) in the 1+MG. The aim of this short paper is to evaluate the applicability of some of the tools of the fair4health project to the Gaslini infrastructure to facilitate its participation in the PoC.

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.

Background: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets.

Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

[This corrects the article DOI: 10.2196/27990.].

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

Background: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits.

A Formal Model for the FAIR4Health Information Architecture.

In the EU project FAIR4Health, a ETL pipeline for the FAIRification of structured health data as well as an agent-based, distributed query platform for the analysis of research hypotheses and the training of machine learning models were developed. The system has been successfully tested in two clinical use cases with patient data from five university hospitals. Currently, the solution is also being considered for use in other hospitals.

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study.

Background: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers.

Objective: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations.

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.

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

The 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.

End User Evaluation of the FAIR4Health Data Curation Tool.

The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method.

A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study.

Background: The evidence-based medicine (EBM) paradigm requires the development of health care professionals' skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients' health care, increase patient safety, and optimize resources use.

Objective: The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice.

Learning Healthcare System for the Prescription of Genetic Testing in the Gynecological Cancer Risk.

Clinical evidence demonstrates that BRCA 1 and BRCA2 mutations can develop a gynecological cancer but genetic testing has a high cost to the healthcare system. Besides, several studies in the literature indicate that performing these genetic tests to the population is not cost-efficient. Currently, our physicians do not have a system to provide them the support for prescribing genetic tests.

Clinical Decision Support using a Terminology Server to improve Patient Safety.

Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation.

Improving integrated care in chronic kidney failure patients with a standard-based interoperability framework.

This paper introduces the evaluation report after fostering a Standard-based Interoperability Framework (SIF) between the Virgen del Rocío University Hospital (VRUH) Haemodialysis (HD) Unit and 5 outsourced HD centres in order to improve integrated care by automatically sharing patients' Electronic Health Record (EHR) and lab test reports. A pre-post study was conducted during fourteen months. The number of lab test reports of both emergency and routine nature regarding to 379 outpatients was computed before and after the integration of the SIF.

Sharing clinical decisions for multimorbidity case management using social network and open-source tools.

Introduction: Social networks applied through Web 2.0 tools have gained importance in health domain, because they produce improvements on the communication and coordination capabilities among health professionals. This is highly relevant for multimorbidity patients care because there is a large number of health professionals in charge of patient care, and this requires to obtain clinical consensus in their decisions.