Towards a systems approach for chronic diseases, based on health state modeling

Abstract

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

Keywords: Markov health state models; Open Science.; Precision Medicine; Regenerative Medicine; chronic diseases; computational modeling; disease progression; systems approach.

Figure 1.. The proposed innovation ecosystem for chronic diseases, with a new platform that engages different health innovation stakeholders, and allows the emergence of interdisciplinary understanding of health states across biology, medicine and health economics in its digital center.

The design is based on the ambition that all stakeholders should benefit from the development of this digital center. RWE = real world evidence.

Figure 2.. Design of combined interventions, as an application of health state modeling.

Health states (HS1-4), which match state definitions in probabilistic Markov models, are connected with interventions (IN1-3), defining the time aspect in the PM vision (“the right intervention for the right patient, at the right time”). Each health state would have annotation in terms of pathobiology, health economics and clinical picture.