Patients with multimorbidity (defined as the co-occurrence of multiple chronic diseases) frequently experience fragmented care, which increases the risk of negative outcomes. A recently proposed Integrated Multimorbidity Care Model aims to overcome many issues related to fragmented care. In the context of Joint Action CHRODIS-PLUS, an implementation methodology was developed for the care model, which is being piloted in five sites. We aim to (1) explain the methodology used to implement the care model and (2) describe how the pilot sites have adapted and applied the proposed methodology. The model is being implemented in Spain (Andalusia and Aragon), Lithuania (Vilnius and Kaunas), and Italy (Rome). Local implementation working groups at each site adapted the model to local needs, goals, and resources using the same methodological steps: (1) Scope analysis; (2) situation analysis—“strengths, weaknesses, opportunities, threats” (SWOT) analysis; (3) development and improvement of implementation methodology; and (4) final development of an action plan. This common implementation strategy shows how care models can be adapted according to local and regional specificities. Analysis of the common key outcome indicators at the post-implementation phase will help to demonstrate the clinical effectiveness, as well as highlight any difficulties in adapting a common Integrated Multimorbidity Care Model in different countries and clinical settings.

Multimorbidity, the coexistence of several chronic conditions in a patient, represents a great challenge for healthcare systems and society. The Integrated Multimorbidity Care Model (IMCM) was recently designed within the Joint Action on chronic diseases and promoting healthy ageing across the life cycle (CHRODIS) to ensure the continuity of care for patients with multimorbidity. The IMCM was implemented in five European pilot sites in Spain, Italy, and Lithuania, within the Joint Action CHRODIS-PLUS. The effect of these pilot interventions was assessed pre- and post-implementation by 17 healthcare managers, using the Assessment of Chronic Illness Care (ACIC) measure, and by 226 patients with the Patient Assessment of Care for Chronic Conditions (PACIC+) survey. The ACIC total score significantly increased (5.23 to 6.71, p = 0.022) after the intervention, with differences across sites. A significant increase in the PACIC+ summary score was found ranging from 3.25 at baseline to 4.03 after the intervention (p < 0.001), and 58% of the sample perceived an improvement in care. Higher PACIC+ scores after the intervention were associated to lower baseline values in the respective PACIC+ dimension and to greater changes in ACIC Part 1 (delivery system organization). The IMCM implementation can help improve the quality of care for patients with multimorbidity.

Background/Objectives: The principles of value-based healthcare (VBHC) have received widespread endorsement, leading healthcare organizations worldwide to shift their strategies towards them. However, despite growing recognition and acceptance, the actual implementation of value-based approaches varies widely. This research aims to identify studies that address the implementation of VBHC at different levels (healthcare policymakers, hospital administrators, and healthcare providers), focusing on each level’s relative strategies. Methods: To this end, a scoping review was conducted in accordance with the PRISMA extension for the scoping reviews checklist. The electronic databases of Web of Science, PubMed, MEDLINE, and Scopus were searched to identify relevant publications in English from January 2006 to 31 July 2023. Results: We identified 30 eligible studies. Findings are organized into four main macro strategic levels, each comprising specific dimensions and operational approaches. Fourteen articles analyzed the role of government commitment in VBHC implementation, while six articles focused on regional integrated care systems. The role of hospitals was described in sixteen records. Conclusions: Our study suggests that a comprehensive approach is necessary for the successful implementation of VBHC. Hospitals emerge as pivotal in this shift, requiring organizational and attitudinal changes among healthcare professionals. However, a complete transition towards VBHC that ensures seamless patient management throughout the entire care delivery value chain necessitates government involvement in terms of state legislation, reimbursement methods, and hospital networking.

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 to offer access to certified FAIR datasets. 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.

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.

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. 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.