Multimorbidity and the Challenge to Deliver Personalised and Meaningful Health Care

Heleno, Bruno; Silvério-Rodrigues, David

doi:10.1159/000502270

Cited by 1 publication

(1 citation statement)

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“…The delivery of care and the patient self-management may be constrained by complicated medication regimens and information burden [ 5 ]. Recommendations that are given to a patient for several single diseases may be mutually conflicting and produce harm rather than good [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Majnarić

Babič

O’Sullivan

et al. 2021

JCM

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Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

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Section: Introductionmentioning

confidence: 99%