An innovative decision-support system for improved care pathways for patients with neurodegenerative diseases and comorbidities

The following impacts are expected:

• Enhanced cross-sectoral collaboration between healthcare industries, academia, and all relevant actors of the healthcare ecosystem (including patients and their organisations, carers, regulators, healthcare professionals/ providers), enabling exchange of resources beyond data (such as analytical tools, material for training and professional development of personnel).
• Earlier and more precise diagnosis, more clinically effective interventions, better patient adherence, and reduced hospitalisation (reduction in re-admission/period of hospitalisation).
• A patient stratification able to better predict clinical outcomes to support the development of more patient-adapted interventions / therapeutics including that of potential emerging disease modifying therapies.
• Better patient clinical outcomes and improved patient experience for patients with neurodegenerative diseases.
• More cost-effective and better prepared care pathway management for patients with neurodegenerative diseases.
• Contribute to the ‘European Health Data Space¹ by promoting better exchange of, and access to, different types of health data and data generated by health technologies (through FAIR principles: findable, accessible, interoperable, and re-usable) for the benefit of European citizens, health researchers and health policy makers.

^{1 http://ec.europa.eu/commission/presscorner/detail/en/ip_22_2711}

R&I actions to be supported under this topic shall contribute to the following outcomes:

• A (sustainable) re-usable, interoperable, easily adaptable, and scalable digital platform, capable of translating a heterogeneous and fragmented set of complex measurable and analysable health data elements into a clinical-decision-support system that can guide patients to better health and quality of life. Initially designed for patients with neurodegenerative diseases and comorbidities, the platform’s easy adaptability ensures its re-use in other health areas for the benefit of healthcare professionals, patients, families, and carers, thereby promoting its wider use.
• A sustainable framework for collaboration across specialities and all relevant stakeholders to foster social innovation to decrease the burden on patients, families, and carers and to develop models to incentivise/maintain collaboration and ensure feasibility of future implementation.
• Effective and agreed standards and guidelines that support both data collection and all operational features of the digital platform enabling health technology developers to create efficient clinical decision support systems for a more patient-centric and optimised delivery of healthcare interventions. Healthcare professionals/providers use these solutions leading to improvements in the healthcare pathways.
• Enhanced, and more reliable tools and methods (e.g., analytical tools and algorithms) able to provide (near) real time feedback on health interventions, including on the usability, efficacy/effectiveness, and the long-term safety of health technologies. Together, these enable healthcare professionals and providers to make more inclusive and efficient patient-centred decisions that, additionally, can aid the development of predictive simulation tools and models.
• Enhanced clinical interpretation of multi-modal, multi-parametric data including socio-economical, which influence variations in the status of the patient with neurodegenerative disease and the required levels of care. This will be benefitting the patients, as a more person-centric treatment and care, and the healthcare providers as optimised allocation of resources, and prediction of how patients’ needs will change due to their co-morbid condition or other precipitating medical factors.

Neurodegenerative disorders represent a high societal burden impacting patients, their families, and public healthcare systems. Patients with a neurodegenerative disorder frequently display at least one comorbidity, which together with the observed polypharmacy creates a highly complex system that needs better understanding to optimise current care pathways. Recent developments give grounds for cautious optimism that a disease-modifying therapy is on the horizon. However, the high disease prevalence, and the complex evaluation process when such a therapy becomes available, will create challenges for already over-burdened healthcare systems. This will increase the demand for and importance of diagnostic and digital solutions that can drive the related clinical pathways and optimise and personalise care delivery.

The primary objective of this topic is to develop a decision-support system to enhance medical decisions with targeted clinical knowledge, patient information, and other health information for a more holistic (better integrating diagnosis, treatment and care and breaking silos across specialities) approach to managing and treating patients with a neurodegenerative disease and a comorbid condition, addressing the needs of today, while creating preparedness for a future paradigm-shift in treatment.

In their proposal, applicants should formulate how to best achieve all the outcomes/outputs of this topic, also describing the expected actual improvement in care and treatment outcomes and reflecting on aspects of implementation into routine care and sustainability, that are barriers to developing and distributing/delivering innovations. This should be preceded by a key stakeholder mapping to grasp the relevant players within this ecosystem and build and leverage as much as possible upon already available resources and learnings.

Proposals should address a patient population with a neurodegenerative disease where there is evidence of the importance of comorbidities in their healthcare pathways and on patient quality of life. The choice of the comorbidity should consider the burden for patients, carers and families, and the availability of medical technology-generated data. Cancer is out of scope.

Applicants should develop a (sustainable) re-usable, interoperable, and scalable digital platform, to safely and efficiently collect, curate, store, share, access, integrate and analyse multimodal longitudinal, dynamic health data generated within and outside the healthcare setting.

This will require breaking existing data silos across different medical specialities to allow the dynamic flow of information on the concomitant conditions and their interplay to improve the selection of the best possible care pathways, and patient adherence.

Data may include medical/laboratory data, automatically collected data, omics data, medical device data, treatment modality/intervention-type data, real-world evidence, including medical condition and lifestyle-related data collected via e-health solutions, smart devices, wearables, medical grade sensors and other patient self-reported data. Data on contextual information, for example on the socioeconomic environment as well as professional and informal caregivers (like availability, roles, interprofessional cooperation, interaction with the patient/client), the setting and organisation of care, staffing, and payment models, should be considered to enrich the dataset informing decision, as well as data from patient registries. Current European activities on digital health and care should be considered when relevant¹. The patient perspective and notably their quality of life, will need to be sufficiently considered including via patient-reported experiences and outcomes measurements (PREMs; PROMs). The perspective of families and carers should be also included.

Applicants should consider leveraging relevant large datasets that are already available at national and / or European level.

Ensuring data quality will be of paramount importance. In addition, applicants should ensure trustworthy and safe sharing of patient data through ‘privacy and security by design’. They should also give ample consideration for the control of data reuse by patients and healthcare professionals, for example by the implementation of ‘FAIR’ data principles and a suitable data governance structure.

The platform should build on suitable existing platforms or elements thereof (for example specialised research infrastructures, including those developed by IMI projects) with proven efficiency and interoperability, complying with European privacy and security requirements and enabling integrated workflows of data management, curation, and analysis to amplify the intrinsic value of the datasets. Its design should allow for future expansion as well as continuous updates in a secure environment, plus potential integration with other platforms and easy adaptation for use in other health areas.

Advanced analytical and workflow tools (including artificial intelligence (AI)-based) and, where relevant, predictive simulations should be proposed which enable improved analysis of the integrated patient data in combination with clinical insights and expertise to optimise best practice guidelines, support better clinical decision-making and assessment of outcomes for optimised care pathways, bespoke to the patient and the healthcare system.

Applicants should also consider how the proposed solutions could be part of integrated community-based health and social services that optimise independence, quality of life and the wellbeing of the individual, including when relevant behavioural changes, while decreasing the burden on families and carers.

Applicants providing data as part of their applications should include in the proposals evidence that all legal, ethical, and intellectual property permissions are in place to ensure the availability of the data to the consortium.

^{1 http://ec.europa.eu/health/ehealth-digital-health-and-care/electronic-cross-border-health-services_en}