Demographic shifts and the rise of multimorbidity 

It is well documented that the demographic changes, notably an increase in life expectancy and an accelerated aging of populations, represent a challenge for the sustainability of healthcare services, with projections indicating an escalation of these challenges in the following years.  It is well documented that the demographic changes, notably an increase in life expectancy and an accelerated aging of populations, represent a challenge for the sustainability of healthcare services, with projections indicating an escalation of these challenges in the following years.  These demographic trends, coupled with advancements in diagnostic accuracy and treatment efficacy, have led to a significant rise in the prevalence of persons with multimorbidity (PMM) - defined as the concurrent manifestation of two or more chronic conditions [1]. Multimorbidity is increasingly becoming a critical challenge for healthcare providers across Europe.  Currently, there are more than 50 million PMM, and as many as 65% of people above 65 years may be affected by multimorbidity. Notably, PMM largely constitute the top 10% of healthcare consumers, a group which accounts for approximately two-thirds of total specialized care expenditures [2], and 90% of patients discharged to municipal care services are identified as PMM [3]. These factors collectively pose a threat to the long-term sustainability of healthcare services. 

Challenges in current healthcare systems 

Current healthcare services operate within a siloed framework, characterized by two major levels of limitation: Firstly, the existing system predominantly supports episodic, reactive care that is tailored to specific diagnoses. The increase in PMM patients demands for shifting from a diagnosis specific to a person-centred, integrated, and proactive care approach. While evidence shows that such an approach enhances patient satisfaction, increases perceived quality of care, and improves access to services, its efficacy for people with multimorbidity remains to be demonstrated. Integrated care ecosystems need to be proactive in supporting people with multimorbidity to address both their health and social care needs, while at the same time minimizing service use and expenditure. Crucially, we need to enable care providers and practitioners to consistently adopt a holistic view of their patients that incorporates social and home environmental factors, contrary to a solely a diagnosis-focused perspective. Such a comprehensive approach enables the anticipation and holistic management of symptoms arising from the interplay of complex, coexisting illnesses. 

Secondly, a remaining key hindrance for improved care, is the lack of support for cross-organizational patient information flow. Despite prior research initiatives that have successfully redesigned PMM care models, a lack of information continuity during patient transitions between various care levels and institutions frequently results in duplicated efforts and uncoordinated care activities. Such inefficiencies not only degrade the quality of care and treatment but also contribute to escalated healthcare expenditures[1, 3]. While most healthcare providers use digital solutions, interoperability between the different system remains inadequate. The emergence of e-health solutions presents new opportunities to manage the challenges outlined above. These technological advancements offer potential solutions to the complexities associated with managing an aging population afflicted with multimorbidity, as well as the persistent variations in care quality and the escalating costs of healthcare. [1, 3]. 

The role of e-health and AI in transforming healthcare 

Building on the potential of e-health solutions, Artificial Intelligence (AI) emerges as a pivotal component for improving healthcare practices, particularly for optimizing digital solutions tailored for care and management of PMM patients. AI-based decision support systems have the potential to significantly improve healthcare delivery to this patient group by reducing administrative burden, predicting treatment efficiency, providing personalized care, and predicting mortality and readmission risk.  

However, while AI holds promise for enhancing chronic care management through improved decision-making and personalized care, its integration in this domain is constrained by several key factors related to data availability and quality, disease complexity, patient engagement and trust. Moreover, the implementation of AI in healthcare settings, especially in chronic care management, requires not only technological readiness but also adaptation within the existing healthcare workflows. This necessitates considerable investment in training and infrastructure, which can be a barrier to the adoption of AI in chronic care management. Addressing these challenges is essential for the successful implementation of AI in the chronic care domain.  

In response to the identified challenges and the potential of AI and digital solutions, the CareMatrix consortium, funded by Horizon 2020, was established as a strategic initiative to develop a sustainable, integrated, proactive care model for multimorbid persons.  The consortium includes key partners from the Regional County Council of Skåne (Sweden), Vestre Viken Hospital Trust (Norway), and Osakidetza public healthcare system (Spain/Basque). In 2023, CareMatrix launched a tender aimed at procuring a digital solution for the integrated care of multimorbid patients. The central objective of this initiative is to position the patient at the forefront of the healthcare process, ensuring that patients, healthcare providers, and other vital stakeholders in the patient's healthcare journey have consistent access to shared information. As of present, no such solution exists in the marked.  

The initial phase of the project began with nine suppliers, narrowing down to five in the subsequent phase. Among these is Doole Health, a Spanish company specializing in a Telehealth Platform that facilitates virtual medical consultations and strengthens the patients-health professional connections. Doole Health is developing INCA Health as their solution to address the tender in CareMatrix in collaboration with Fight Infections Foundation, Germans Trias i Pujol Research Institute, Faculty of Health Sciences at Open University of Catalonia, and the Norwegian Centre for E-health Research.  

Advancing integrated care solutions for multimorbidity through the CareMatrix initiative 

The INCA Solution is designed to manage the integrated care for PMM patients. It incorporates patient’s data collected from diverse sources, including social and health information, and provides tools for sharing care plans between professionals, patients, and caregivers. The Shared Care Plan facilitates centralized coordination between different care levels, enabling real-time adaptation of plans by the entire professional team of caregivers. A Multidisciplinary Care Centre oversees PMM patient care, focusing on the rationalization of visits and medication reviews from a holistic perspective. The INCA Solution introduces a patient-centric multichannel communication platform, equipped with an alarm and alert system featuring both quantitative and qualitative triggers for early detection of patient decompensation. Additionally, an AI-based clinical decision support system within INCA enhances attention, self-management and coordination for PMM, incorporating automated processes triggered to reduce unjustified clinical variability. From the patient’s perspective, the INCA Solution offers a specialized application, tailored to different patient profiles, as well as to caregivers and relatives, empowering them to manage their information and shared care plans effectively. 

References

erntsen, G.K.R., et al., Person-centred, integrated and pro-active care for multi-morbid elderly with advanced care needs: a propensity score-matched controlled trial. BMC health services research, 2019. 19(1): p. 1-17. 

2.    Wang, L., et al., A systematic review of cost-of-illness studies of multimorbidity. Applied health economics and health policy, 2018. 16(1): p. 15-29. 

3.    Grimsmo, A., et al., Disease-specific clinical pathways–are they feasible in primary care? A mixed-methods study. Scandinavian journal of primary health care, 2018. 36(2): p. 52-160. 

4.    Singareddy, S., et al., Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus, 2023. 15(9). 

5.    Reddy, S., J. Fox, and M.P. Purohit, Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 2019. 112(1): p. 22-28. 

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